Event-based Vision, Event Cameras, Event Camera SLAM

Event cameras, such as the Dynamic Vision Sensor (DVS), are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied, so that new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required.


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CVPR 2021 Workshop on Event-based Vision


On June 19th, 2021, Guillermo Gallego (TU Berlin), Davide Scaramuzza (UZH), and Kostas Daniilidis (UPENN), Cornelia Fermueller (Univ. of Maryland), Davide Migliore (Prophesee) organized the 3rd International Workshop on Event-based Vision at CVPR 2021. The event took place online. Check out the video recordings, slides, proceedings and live demos.


CVPR 2019 Workshop on Event-based Vision and Smart Cameras


On June 17th, 2019, Davide Scaramuzza (RPG), Guillermo Gallego (RPG), and Kostas Daniilidis (UPenn) organized the 2nd International Workshop on Event-based Vision and Smart Cameras at CVPR in Long Beach. Check out the video recordings, slides, proceedings and live demos.


ICRA 2017 Workshop on Event-based Vision


On June 2nd, 2017, Davide Scaramuzza (RPG), Guillermo Gallego (RPG), and Andrea Censi (ETH) organized the 1st International Workshop on Event-based Vision at ICRA in Singapore. Check out the slides and proceedings.




Multi-Bracket High Dynamic Range Imaging with Event Cameras

Multi-Bracket High Dynamic Range Imaging with Event Cameras

Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times. While these methods work well in static scenes, dynamic scenes remain a challenge since the LDR images still suffer from saturation and noise. In such scenarios, event cameras would be a valid complement, thanks to their higher temporal resolution and dynamic range. In this paper, we propose the first multi- bracket HDR pipeline combining a standard camera with an event camera. Our results show better overall robustness when using events, with improvements in PSNR by up to 5dB on synthetic data and up to 0.7dB on real-world data. We also introduce a new dataset containing bracketed LDR images with aligned events and HDR ground truth.


References

Multi-Bracket High Dynamic Range Imaging with Event Cameras

N. Messikommer*, S. Georgoulis*, D. Gehrig, S. Tulyakov, J. Erbach, A. Bochicchio, Y. Li, D. Scaramuzza

Multi-Bracket High Dynamic Range Imaging with Event Cameras

IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), New Orleans, 2022.

PDF YouTube


Event-aided Direct Sparse Odometry

Event-aided Direct Sparse Odometry

We introduce EDS, a direct monocular visual odometry using events and frames. Our algorithm leverages the event generation model to track the camera motion in the blind time between frames. The method formulates a direct probabilistic approach of observed brightness increments. Per-pixel brightness increments are predicted using a sparse number of selected 3D points and are compared to the events via the brightness increment error to estimate camera motion. The method recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the first method to perform 6-DOF VO using events and frames with a direct approach. By design it overcomes the problem of changing appearance in indirect methods. We also show that, for a target error performance, EDS can work at lower frame rates than state-of-the-art frame-based VO solutions. This opens the door to low-power motion-tracking applications where frames are sparingly triggered "on demand'' and our method tracks the motion in between. We release code and datasets to the public.


References

EDS

J. Hidalgo-Carrió, G.Gallego, D. Scaramuzza

Event-aided Direct Sparse Odometry

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Oral Presentation.

PDF YouTube Code Dataset


Exploring Event Camera-based Odometry for Planetary Robots

Exploring Event Camera-based Odometry for Planetary Robots

Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle, since they cannot cope with significant depth uncertainties caused by an unforeseen loss of tracking or other effects. In this work, we introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based frontend with a filter-based backend. This makes it both accurate and robust to uncertainties, outperforming event- and frame-based VIO algorithms on challenging benchmarks by 32%. In addition, we demonstrate accurate performance in hover-like conditions (outperforming existing event-based methods) as well as high robustness in newly collected Mars-like and high-dynamic-range sequences, where existing frame-based methods fail. In doing so, we show that event-based VIO is the way forward for vision-based exploration on Mars.


References

Exploring Event Camera-based Odometry for Planetary Robots

F. Mahlknecht, D. Gehrig, J. Nash, F. M. Rockenbauer, B. Morrell, J. Delaune and D. Scaramuzza

Exploring Event Camera-based Odometry for Planetary Robots

arXiv, 2022

PDF Code & Datasets


Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion

Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion

Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and faraway scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion—which can be efficiently sampled for image warping—from events and images. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and up to 15% in LPIPS score.


References

AEGNN: Asynchronous Event-based Graph Neural Networks

S. Tulyakov, A. Bochicchio, D. Gehrig, S. Georgoulis, Y. Li, D. Scaramuzza

Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion

IEEE Conference of Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans, USA.

PDF YouTube Dataset Project Webpage


Dense Continuous-Time Optical Flow from Events and Frames

Dense Continuous-Time Optical Flow from Events and Frames

We present a method for estimating dense continuous-time optical flow. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. In this work, we show that it is possible to compute per-pixel, continuous-time optical flow by additionally using events from an event camera. Events provide temporally fine-grained information about movement in image space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous-time via parameterized Bézier curves. To achieve this, we introduce multiple innovations to build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. The proposed approach outperforms existing image-based and event-based methods by 11.5 % lower EPE on DSEC-Flow. Finally, we introduce a novel synthetic dataset MultiFlow for pixel trajectory regression on which our method is currently the only successful approach.


References

Dense Continuous-Time Optical Flow from Events and Frames

Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza

Dense Continuous-Time Optical Flow from Events and Frames

arXiv, 2022.

PDF


AEGNN: Asynchronous Event-based Graph Neural Networks

The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and high temporal resolution of events, leading to high computational burden and latency. For this reason, recent works have adopted Graph Neural Networks (GNNs), which process events as “static” spatio-temporal graphs, which are inherently ”sparse”. We take this trend one step further by introducing Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel event-processing paradigm that generalizes standard GNNs to process events as "evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that restrict recomputation of network activations only to the nodes affected by each new event, thereby significantly reducing both computation and latency for event- by-event processing. AEGNNs are easily trained on synchronous inputs and can be converted to efficient, ”asynchronous” networks at test time. We thoroughly validate our method on object classification and detection tasks, where we show an up to a 200-fold reduction in computational complexity (FLOPs), with similar or even better performance than state-of-the-art asynchronous methods. This reduction in computation directly translates to an 8-fold reduction in computational latency when compared to standard GNNs, which opens the door to low-latency event-based processing.


References

AEGNN: Asynchronous Event-based Graph Neural Networks

S. Schaefer*, D. Gehrig*, D. Scaramuzza

AEGNN: Asynchronous Event-based Graph Neural Networks

IEEE Conference of Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans, USA.

PDF YouTube Code Project Webpage


ESS: Learning Event-based Semantic Segmentation from Still Images

ESS: Learning Event-based Semantic Segmentation from Still Images

Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image degradations. Event cameras promise to address these challenges since they feature a much higher dynamic range and are resilient to motion blur. Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the novelty of the sensor, and the lack of high- quality, labeled datasets. In this work, we introduce ESS, which tackles this problem by directly transferring the semantic segmentation task from existing labeled image datasets to unlabeled events via unsupervised domain adaptation (UDA). Compared to existing UDA methods, our approach aligns recurrent, motion-invariant event embeddings with image embeddings. For this reason, our method neither requires video data nor per-pixel alignment between images and events and, crucially, does not need to hallucinate motion from still images. Additionally, to spur further research in event-based semantic segmentation, we intro- duce DSEC-Semantic, the first large-scale event-based dataset with fine- grained labels. We show that using image labels alone, ESS outperforms existing UDA approaches, and when combined with event labels, it even outperforms state-of-the-art supervised approaches on both DDD17 and DSEC-Semantic. Finally, ESS is general-purpose, which unlocks the vast amount of existing labeled image datasets and paves the way for new and exciting research directions in new fields previously inaccessible for event cameras.


References

ESS: Learning Event-based Semantic Segmentation from Still Images

Z. Sun*, N. Messikommer*, D. Gehrig, D. Scaramuzza

ESS: Learning Event-based Semantic Segmentation from Still Images

arXiv, 2022.

PDF


Are High-Resolution Cameras Really Needed?

Are High Resolution Cameras Really Needed?

Due to their outstanding properties in challenging conditions, event cameras have become indispensable in a wide range of applications, ranging from automotive, computational photography, and SLAM. However, as further improvements are made to the sensor design, modern event cameras are trending toward higher and higher sensor resolutions, which result in higher bandwidth and computational requirements on downstream tasks. Despite this trend, the benefits of using high-resolution event cameras to solve standard computer vision tasks are still not clear. In this work, we report the surprising discovery that, in low-illumination conditions and at high speeds, low-resolution cameras can outperform high-resolution ones, while requiring a significantly lower bandwidth. We provide both empirical and theoretical evidence for this claim, which indicates that high-resolution event cameras exhibit higher per-pixel event rates, leading to higher temporal noise in low-illumination conditions and at high speeds. As a result, in most cases, high-resolution event cameras show a lower task performance, compared to lower resolution sensors in these conditions. We empirically validate our findings across several tasks, namely image reconstruction, optical flow estimation, and camera pose tracking, both on synthetic and real data. We believe that these findings will provide important guidelines for future trends in event camera development.


References

Are High-Resolution Cameras Really Needed?

D. Gehrig, D. Scaramuzza

Are High Resolution Cameras Really Needed?

arXiv, 2022.

PDF YouTube Project Webpage


Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation

Bridging the Gap between Events and Frames through UDA

Event cameras are novel sensors with outstanding properties such as high temporal resolution and high dynamic range. Despite these characteristics, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras. To overcome this drawback, we propose a task transfer method that allows models to be trained directly with labeled images and unlabeled event data. Compared to previous approaches, (i) our method transfers from single images to events instead of high frame rate videos, and (ii) does not rely on paired sensor data. To achieve this, we leverage the generative event model to split event features into content and motion features. This feature split enables to efficiently match the latent space for events and images, which is crucial for a successful task transfer. Thus, our approach unlocks the vast amount of existing image datasets for the training of event-based neural networks. Our task transfer method consistently outperforms methods applicable in the Unsupervised Domain Adaptation setting for object detection by 0.26 mAP (increase by 93%) and classification by 2.7% accuracy.


References

Bridging the Gap between Events and Frames through UDA

N. Messikommer, D. Gehrig, M. Gehrig, D. Scaramuzza

Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation

Robotics and Automation Letters (RAL), 2022.

PDF Code Youtube


ESL: Event-based Structured Light

ESL: Event-based Structured Light

Event cameras are bio-inspired sensors providing significant advantages over standard cameras such as low latency, high temporal resolution, and high dynamic range. We propose a novel structured-light system using an event camera to tackle the problem of accurate and high-speed depth sensing. Our setup consists of an event camera and a laser-point projector that uniformly illuminates the scene in a raster scanning pattern during 16 ms. Previous methods match events independently of each other, and so they deliver noisy depth estimates at high scanning speeds in the presence of signal latency and jitter. In contrast, we optimize an energy function designed to exploit event correlations, called spatio-temporal consistency. The resulting method is robust to event jitter and therefore performs better at higher scanning speeds. Experiments demonstrate that our method can deal with high-speed motion and outperform state-of-the-art 3D reconstruction methods based on event cameras, reducing the RMSE by 83% on average, for the same acquisition time.


References

3DV21_Muglikar

M.Muglikar, G. Gallego D. Scaramuzza

ESL: Event-based Structured Light

International Conference on 3D Vision (3DV), 2021.

PDF Video Code Project Page Poster


Event Guided Depth Sensing

Event-Guided Depth estimation

Active depth sensors like structured light, lidar, and time-of-flight systems sample the depth of the entire scene uniformly at a fixed scan rate. This leads to limited spatio-temporal resolution where redundant static information is over-sampled and precious motion information might be under-sampled. In this paper, we present an efficient bio-inspired event-camera-driven depth estimation algorithm. In our approach, we dynamically illuminate areas of interest densely, depending on the scene activity detected by the event camera, and sparsely illuminate areas in the field of view with no motion. The depth estimation is achieved by an event-based structured light system consisting of a laser point projector coupled with a second event-based sensor tuned to detect the reflection of the laser from the scene. We show the feasibility of our approach in a simulated autonomous driving scenario and real indoor sequences using our prototype. We show that, in natural scenes like autonomous driving and indoor environments, moving edges correspond to less than 10% of the scene on average. Thus our setup requires the sensor to scan only 10% of the scene, which could lead to almost 90% less power consumption by the illumination source. While we present the evaluation and proof-of-concept for an event-based structured-light system, the ideas presented here are applicable for a wide range of depth sensing modalities like LIDAR, time-of-flight, and standard stereo.


References

3DV21_Muglikar

M. Muglikar, D. Moeys, D. Scaramuzza

Event Guided Depth Sensing

International Conference on 3D Vision (3DV), 2021.

PDF Video


E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras

We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Modern frame-based optical flow methods heavily rely on matching costs computed from feature correlation. In contrast, there exists no optical flow method for event cameras that explicitly computes matching costs. Instead, learning-based approaches using events usually resort to the U-Net architecture to estimate optical flow sparsely. Our key finding is that introducing correlation features significantly improves results compared to previous methods that solely rely on convolution layers. Compared to the state-of-the-art, our proposed approach computes dense optical flow and reduces the end-point error by 23% on MVSEC. Furthermore, we show that all existing optical flow methods developed so far for event cameras have been evaluated on datasets with very small displacement fields with a maximum flow magnitude of 10 pixels. We introduce a new real-world dataset that exhibits displacement fields with magnitudes up to 210 pixels and 3 times higher camera resolution based on this observation. Our proposed approach reduces the end-point error on this dataset by 66%.


References

Dense Optical Flow from Event Cameras

M. Gehrig, M. Millhaeusler, D. Gehrig, D. Scaramuzza

E-RAFT: Dense Optical Flow from Event Cameras

International Conference on 3D Vision (3DV), 2021.

Oral Presentation. Oral Acceptance Rate: 13.2%.

Project Page PDF Code Dataset Benchmark Youtube


Event-driven Vision and Control for UAVs on a Neuromorphic Chip

Event-based vision enables ultra-low latency visual feedback and low power consumption, which are key requirements for high-speed control of unmanned aerial vehicles. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with lower latency than images coming from conventional cameras, enabling ultrafast vision-driven control. Here, we explore how an event-based vision algorithm can be integrated with a spiking neural network-based controller. When both are implemented on the same neuromorphic chip, seamless integration of perception and motor control can be demonstrated, as well as efficient online adaptation of the controller. Our spiking neural network on chip is the first example of a vision-based fully neuromorphic controller solving a high-speed control task. The excellent scalability of processing in neuromorphic hardware enables solving more challenging control tasks in the future, such as navigation in cluttered environments, vision-driven swarm robotics, or high-speed inspection and monitoring of infrastructure.


References

ICRA21_Vitale

A. Vitale, A. Renner, C. Nauer, D. Scaramuzza, Y. Sandamirskaya

Event-driven Vision and Control for UAVs on a Neuromorphic Chip

IEEE International Conference on Robotics and Automation (ICRA), Xi'an, 2021.

PDF YouTube Slides


Powerline Tracking with Event Cameras

IROS21_Dietsche_small

Autonomous inspection of powerlines with quadrotors is challenging. Flights require persistent perception to keep a close look at the lines. We propose a method that uses event cameras to robustly track powerlines. Event cameras are inherently robust to motion blur, have low latency, and high dynamic range. Such properties are advantageous for autonomous inspection of powerlines with drones, where fast motions and challenging illumination conditions are ordinary. Our method identifies lines in the stream of events by detecting planes in the spatio-temporal signal, and tracks them through time. The implementation runs onboard and is capable of detecting multiple distinct lines in real time with rates of up to 320 thousand events per second. The performance is evaluated in real-world flights along a powerline. The tracker is able to persistently track the powerlines, with a mean lifetime of the line 10x longer than existing approaches.


References

IROS21_Dietsche

A. Dietsche, G. Cioffi, J. Hidalgo-Carrio, D. Scaramuzza

Powerline Tracking with Event Cameras

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, 2021.

PDF Code Dataset Video IROS 2021 Video Pitch Slides


TimeLens: Event-based Video Frame Interpolation

State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames. In the absence of additional information, first-order approximations, i.e. optical flow, must be used, but this choice restricts the types of motions that can be modeled, leading to errors in highly dynamic scenarios. Event cameras are novel sensors that address this limitation by providing auxiliary visual information in the blind-time between frames. They asynchronously measure per-pixel brightness changes and do this with high temporal resolution and low latency. Event-based frame interpolation methods typically adopt a synthesis-based approach, where predicted frame residuals are directly applied to the key-frames. However, while these approaches can capture non-linear motions they suffer from ghosting and perform poorly in low-texture regions with few events. Thus, synthesis-based and flow-based approaches are complementary. In this work, we introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both. We extensively evaluate our method on three synthetic and two real benchmarks where we show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods. Finally, we release a new large-scale dataset in highly dynamic scenarios, aimed at pushing the limits of existing methods.


References

TimeLens: Event-based Video Frame Interpolation

S. Tulyakov*, D. Gehrig*, S. Georgoulis, J. Erbach, M. Gehrig, Y. Li, D. Scaramuzza

TimeLens: Event-based Video Frame Interpolation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 2021.

PDF Video Code Project Page and Dataset Slides


How to Calibrate Your Event Camera

How to Calibrate Your Event Camera

We propose a generic event camera calibration frame-work using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network–based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras. The advantage of our proposed approach is that we can use standard calibration patterns that do not rely on active illumination. Furthermore, our approach enables the possibility to perform extrinsic calibration between frame- based and event-based sensors without additional complexity. Both simulation and real-world experiments indicate that calibration through image reconstruction is accurate under common distortion models and a wide variety of distortion parameters.


References

How to Calibrate Your Event Camera

M.Muglikar*,M. Gehrig*, D. Gehrig, D. Scaramuzza

How to Calibrate Your Event Camera

IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), Nashville, 2021

PDF Video Code


DSEC: A Stereo Event Camera Dataset for Driving Scenarios

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sunset remains an open problem. In these cases, standard cameras are being pushed to their limits in terms of low light and high dynamic range performance. To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data. DSEC offers data from a wide-baseline stereo setup of two color frame cameras and two high-resolution monochrome event cameras. In addition, we collect lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of high-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high resolution, large scale stereo dataset with event cameras. The dataset contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms.


References

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

M. Gehrig, W. Aarents, D. Gehrig, D. Scaramuzza

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

IEEE Robotics and Automation Letters (RA-L), 2021.

PDF Project Page and Dataset Code Teaser ICRA 2021 Video Pitch Slides


Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras due to their high temporal resolution, high dynamic range and lack of motion blur. However, events only measure the varying component of the visual signal, which limits their ability to encode scene context. By contrast, standard cameras measure absolute intensity frames, which capture a much richer representation of the scene. Both sensors are thus complementary. However, due to the asynchronous nature of events, combining them with synchronous images remains challenging, especially for learning-based methods. This is because traditional recurrent neural networks (RNNs) are not designed for asynchronous and irregular data from additional sensors. To address this challenge, we introduce Recurrent Asynchronous Multimodal (RAM) networks, which generalize traditional RNNs to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We apply this novel architecture to monocular depth estimation with events and frames where we show an improvement over state-of-the-art methods by up to 30\% in terms of mean absolute depth error. To enable further research on multimodal learning with events, we release EventScape, a new dataset with events, intensity frames, semantic labels, and depth maps recorded in the CARLA simulator.



References

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

D. Gehrig*, M. Rüegg*, M. Gehrig, J. Hidalgo-Carrió, D. Scaramuzza

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

IEEE Robotics and Automation Letters (RA-L), 2021.

PDF Code Project Page ICRA 2021 Video Pitch Slides


Learning Monocular Dense Depth from Events

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. Compared to conventional image sensors, they offer significant advantages: high temporal resolution, high dynamic range, no motion blur, and much lower bandwidth. Recently, learning-based approaches have been applied to event-based data, thus unlocking their potential and making significant progress in a variety of tasks, such as monocular depth prediction. Most existing approaches use standard feed-forward architectures to generate network predictions, which do not leverage the temporal consistency presents in the event stream. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods. In particular, our method generates dense depth predictions using a monocular setup, which has not been shown previously. We pretrain our model using a new dataset containing events and depth maps recorded in the CARLA simulator. We test our method on the Multi Vehicle Stereo Event Camera Dataset (MVSEC). Quantitative experiments show up to 50% improvement in average depth error with respect to previous event-based methods.


References

Learning Monocular Dense Depth from Events

J. Hidalgo-Carrió, D. Gehrig, D. Scaramuzza

Learning Monocular Dense Depth from Events

IEEE International Conference on 3D Vision (3DV), 2020.

PDF Code Project Page


Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation

ICPR20_Kostadinov

Event-based cameras record an asynchronous stream of per-pixel brightness changes. As such, they have numerous advantages over the standard frame-based cameras,including high temporal resolution, high dynamic range, and no motion blur. Due to the asynchronous nature, efficient learning of compact representation for event data is challenging. While it remains not explored the extent to which the spatial and temporal event "information" is useful for pattern recognition tasks. Inthis paper, we focus on single-layer architectures. We analyze the performance of two general problem formulations: the directand the inverse, for unsupervised feature learning from local event data (local volumes of events described in space-time).We identify and show the main advantages of each approach.Theoretically, we analyze guarantees for an optimal solution,possibility for asynchronous, parallel parameter update, and the computational complexity. We present numerical experiments for object recognition. We evaluate the solution under the direct and the inverse problem and give a comparison with the state-of-the-art methods. Our empirical results highlight the advantages of both approaches for representation learning from event data. Weshow improvements of up to 9%in the recognition accuracy compared to the state-of-the-art methods from the same class of methods.


References

ICPR20_Kostadinov

D. Kostadinov, D. Scaramuzza

Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation

IAPR IEEE/Computer Society International Conference on Pattern Recognition (ICPR), Milan, 2021.

PDF


Event-based Asynchronous Sparse Convolutional Networks

Event cameras are bio-inspired sensors that respond to per-pixel brightness changes in the form of asynchronous and sparse "events". Recently, pattern recognition algorithms, such as learning-based methods, have made significant progress with event cameras by converting events into synchronous dense, image-like representations and applying traditional machine learning methods developed for standard cameras. However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency. In this work, we present a general framework for converting models trained on synchronous image-like event representations into asynchronous models with identical output, thus directly leveraging the intrinsic asynchronous and sparse nature of the event data. We show both theoretically and experimentally that this drastically reduces the computational complexity and latency of high-capacity, synchronous neural networks without sacrificing accuracy. In addition, our framework has several desirable characteristics: (i) it exploits spatio-temporal sparsity of events explicitly, (ii) it is agnostic to the event representation, network architecture, and task, and (iii) it does not require any train-time change, since it is compatible with the standard neural networks' training process. We thoroughly validate the proposed framework on two computer vision tasks: object detection and object recognition. In these tasks, we reduce the computational complexity up to 20 times with respect to high-latency neural networks. At the same time, we outperform state-of-the-art asynchronous approaches up to 24% in prediction accuracy.


References

ArXiv Preprint

Nico Messikommer, Daniel Gehrig, Antonio Loquercio, and Davide Scaramuzza

Event-based Asynchronous Sparse Convolutional Networks

European Conference on Computer Vision (ECCV), Glasgow, 2020.

PDF YouTube ECCV20 Presentation Code


Dynamic Obstacle Avoidance for Quadrotors with Event Cameras

Today's autonomous drones have reaction times of tens of milliseconds, which is not enough for navigating fast in complex dynamic environments. To safely avoid fast moving objects, drones need low-latency sensors and algorithms. We departed from state-of-the-art approaches by using event cameras, which are bioinspired sensors with reaction times of microseconds. Our approach exploits the temporal information contained in the event stream to distinguish between static and dynamic objects and leverages a fast strategy to generate the motor commands necessary to avoid the approaching obstacles. Standard vision algorithms cannot be applied to event cameras because the output of these sensors is not images but a stream of asynchronous events that encode per-pixel intensity changes. Our resulting algorithm has an overall latency of only 3.5 milliseconds, which is sufficient for reliable detection and avoidance of fast-moving obstacles. We demonstrate the effectiveness of our approach on an autonomous quadrotor using only onboard sensing and computation. Our drone was capable of avoiding multiple obstacles of different sizes and shapes, at relative speeds up to 10 meters/second, both indoors and outdoors.


References

Science20_Falanga

Davide Falanga, Kevin Kleber, and Davide Scaramuzza

Dynamic Obstacle Avoidance for Quadrotors with Event Cameras

Science Robotics, March 18, 2020.

PDF Supplementary Material YouTube


Event-Based Angular Velocity Regression with Spiking Networks

SNN

Spiking Neural Networks (SNNs) are bio-inspired networks that process information conveyed as temporal spikes rather than numeric values. An example of a sensor providing such data is the event camera. It only produces an event when a pixel reports a significant brightness change. Similarly, the spiking neuron of an SNN only produces a spike whenever a significant number of spikes occur within a short period of time. Due to their spike-based computational model, SNNs can process output from event-based, asynchronous sensors without any pre-processing at extremely lower power unlike standard artificial neural networks. This is possible due to specialized neuromorphic hardware that implements the highly-parallelizable concept of SNNs in silicon. Yet, SNNs have not enjoyed the same rise of popularity as artificial neural networks. This not only stems from the fact that their input format is rather unconventional but also due to the challenges in training spiking networks. Despite their temporal nature and recent algorithmic advances, they have been mostly evaluated on classification problems. We propose, for the first time, a temporal regression problem of numerical values given events from an event camera.

We specifically investigate the prediction of the 3-DOF angular velocity of a rotating event camera with an SNN. The difficulty of this problem arises from the prediction of angular velocities continuously in time directly from irregular, asynchronous event-based input. Directly utilising the output of event cameras without any pre-processing ensures that we inherit all the benefits that they provide over conventional cameras. That is high-temporal resolution, high-dynamic range and no motion blur. To assess the performance of SNNs on this task, we introduce a synthetic event camera dataset generated from real-world panoramic images and show that we can successfully train an SNN to perform angular velocity regression.


References

SNN

M. Gehrig, S. Shrestha, D. Mouritzen, D. Scaramuzza

Event-Based Angular Velocity Regression with Spiking Networks

IEEE International Conference on Robotics and Automation (ICRA), 2020

PDF Code YouTube


Video to Events: Recycling Video Dataset for Event Cameras

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high dynamic range (HDR), high temporal resolution, and no motion blur. Recently, novel learning approaches operating on event data have achieved impressive results. Yet, these methods require a large amount of event data for training, which is hardly available due the novelty of event sensors in computer vision research. In this paper, we present a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to \emph{synthetic} event data. This unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data. We evaluate our method on two relevant vision tasks, i.e., object recognition and semantic segmentation, and show that models trained on synthetic events have several benefits: (i) they generalize well to real event data, even in scenarios where standard-camera images are blurry or overexposed, by inheriting the outstanding properties of event cameras; (ii) they can be used for fine-tuning on real data to improve over state-of-the-art for both classification and semantic segmentation.


References

Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras

D. Gehrig, M. Gehrig, J. Hidalgo-Carrio, D. Scaramuzza

Video to Events: Recycling Video Dataset for Event Cameras

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2020.

PDF YouTube CVPR20 Video Pitch Code


Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras

Event cameras are a promising candidate to enable high speed vision-based control due to their low sensor latency and high temporal resolution. However, purely event-based feedback has yet to be used in the control of drones. In this work, a first step towards implementing low-latency high-bandwidth control of quadrotors using event cameras is taken. In particular, this paper addresses the problem of one-dimensional attitude tracking using a dualcopter platform equipped with an event camera. The event-based state estimation consists of a modified Hough transform algorithm combined with a Kalman filter that outputs the roll angle and angular velocity of the dualcopter relative to a horizon marked by a black-and-white disk. The estimated state is processed by a proportional-derivative attitude control law that computes the rotor thrusts required to track the desired attitude. The proposed attitude tracking scheme shows promising results of event-camera-driven closed loop control: the state estimator performs with an update rate of 1 kHz and a latency determined to be 12 ms, enabling attitude tracking at speeds of over 1600 degrees per second.


References

Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras

R. Sugimoto, M. Gehrig, D. Brescianini, D. Scaramuzza

Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras

IEEE International Conference on Robotics and Automation (ICRA), 2020

PDF YouTube ICRA2020 Video Pitch


Event-Based Motion Segmentation by Motion Compensation

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.


References

Event-Based Motion Segmentation by Motion Compensation

T. Stoffregen, G. Gallego, T. Drummond, L. Kleeman, D. Scaramuzza

Event-Based Motion Segmentation by Motion Compensation

IEEE International Conference on Computer Vision (ICCV), 2019.

PDF (animations best viewed with Acrobat Reader) YouTube


End-to-End Learning of Representations for Asynchronous Event-Based Data

End-to-End Learning of Representations for Asynchronous Event-Based Data

Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.


References

End-to-End Learning of Representations for Asynchronous Event-Based Data

D. Gehrig, A. Loquercio, K. G. Derpanis, D. Scaramuzza

End-to-End Learning of Representations for Asynchronous Event-Based Data

IEEE International Conference on Computer Vision (ICCV), 2019.

PDF YouTube Code


High Speed and High Dynamic Range Video with an Event Camera

Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous events instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images.

In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams.

Our quantitative experiments show that our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality (> 20%), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos (> 5,000 frames per second) of high-speed phenomena (e.g. a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. As an additional contribution, we demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data. We release the reconstruction code and a pre-trained model to enable further research.

We presented our approach in two different papers (references below). Our first paper (CVPR19) introduced the network architecture (a simple recurrent neural network), the training data, and our first video reconstruction results. In our follow-up paper (T-PAMI), we improved the network architecture by using convolutional LSTM blocks and a temporal consistency loss, leading to improved stability and temporal consistency. Furthermore, the improved network now works well with windows containing variable number of events, which allows to synthesize videos at a very high framerate (> 5,000 frames per second), which we additionally demonstrated in a series of new experiments featuring extremely fast motions.


References

High Speed and High Dynamic Range Video with an Event Camera

H. Rebecq, R. Ranftl, V. Koltun, D. Scaramuzza

High Speed and High Dynamic Range Video with an Event Camera

IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2020.

PDF YouTube Code Project Page


Fast Image Reconstruction with an Event Camera

C. Scheerlinck, H. Rebecq, D. Gehrig, N. Barnes, R. Mahony, D. Scaramuzza

Fast Image Reconstruction with an Event Camera

IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.

PDF YouTube Code and Datasets


Events-to-Video: Bringing Modern Computer Vision to Event Cameras

H. Rebecq, R. Ranftl, V. Koltun, D. Scaramuzza

Events-to-Video: Bringing Modern Computer Vision to Event Cameras

IEEE International Conference on Pattern Recognition (CVPR), 2019.

PDF YouTube


Event-based Vision: A Survey

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that work radically different from traditional cameras. Instead of capturing images at a fixed rate, they measure per-pixel brightness changes asynchronously. This results in a stream of events, which encode the time, location and sign of the brightness changes. Event cameras posses outstanding properties compared to traditional cameras: very high dynamic range (140 dB vs. 60 dB), high temporal resolution (in the order of microseconds), low power consumption, and do not suffer from motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as high speed and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.


References

Event-based Vision: A Survey

G. Gallego, T. Delbruck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. Davison, J. Conradt, K. Daniilidis, D. Scaramuzza

Event-based Vision: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.

PDF


How Fast is Too Fast? The Role of Perception Latency in High-Speed Sense and Avoid

In this work, we study the effects that perception latency has on the maximum speed a robot can reach to safely navigate through an unknown cluttered environment. We provide a general analysis that can serve as a baseline for future quantitative reasoning for design trade-offs in autonomous robot navigation. We consider the case where the robot is modeled as a linear second-order system with bounded input and navigates through static obstacles. Also, we focus on a scenario where the robot wants to reach a target destination in as little time as possible, and therefore cannot change its longitudinal velocity to avoid obstacles. We show how the maximum latency that the robot can tolerate to guarantee safety is related to the desired speed, the range of its sensing pipeline, and the actuation limitations of the platform (i.e., the maximum acceleration it can produce). As a particular case study, we compare monocular and stereo frame-based cameras against novel, low-latency sensors, such as event cameras, in the case of quadrotor flight. To validate our analysis, we conduct experiments on a quadrotor platform equipped with an event camera to detect and avoid obstacles thrown towards the robot. To the best of our knowledge, this is the first theoretical work in which perception and actuation limitations are jointly considered to study the performance of a robotic platform in high-speed navigation.


References

How Fast is Too Fast? The Role of Perception Latency in High-Speed Sense and Avoid

D. Falanga, S. Kim, D. Scaramuzza

How Fast is Too Fast? The Role of Perception Latency in High-Speed Sense and Avoid

IEEE Robotics and Automation Letters (RA-L), 2019.

PDF YouTube


CED: Color Event Camera Dataset

CED: Color Event Camera Dataset

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called "events". Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Until recently, event cameras have been limited to outputting events in the intensity channel, however, recent advances have resulted in the development of color event cameras, such as the Color DAVIS346. In this work, we present and release the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes, which we hope will help drive forward event-based vision research. We also present an extension of the event camera simulator ESIM that enables simulation of color events. Finally, we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color DAVIS346 into a continuous-time, HDR, color video camera to visualise the event stream, and for use in downstream vision applications.


References

CED_image

C. Scheerlinck*, H. Rebecq*, T. Stoffregen, N. Barnes, R. Mahony, D. Scaramuzza

CED: Color Event Camera Dataset

IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.

PDF YouTube Dataset


Focus Is All You Need: Loss Functions for Event-based Vision

Focus Is All You Need: Loss Functions for Event-based Vision

Event cameras are novel vision sensors that output pixel-level brightness changes ("events") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches. We call them focus loss functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras.


References

Focus Is All You Need: Loss Functions for Event-based Vision

G. Gallego, M. Gehrig, D. Scaramuzza

Focus Is All You Need: Loss Functions for Event-based Vision

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 2019.

PDF Poster YouTube


The UZH-FPV Drone Racing Dataset

Despite impressive results in visual-inertial state estimation in recent years, high speed trajectories with six degree of freedom motion remain challenging for existing estimation algorithms. Aggressive trajectories feature large accelerations and rapid rotational motions, and when they pass close to objects in the environment, this induces large apparent motions in the vision sensors, all of which increase the difficulty in estimation.

We introduce the UZH-FPV Drone Racing dataset, consisting of over 27 sequences, with more than 10 km of flight distance, captured on a first-person-view (FPV) racing quadrotor flown by an expert pilot. The dataset features event camera data, camera images, and inertial measurements, together with precise ground truth poses. These sequences are faster and more challenging, in terms of apparent scene motion, than any existing dataset.


References

Information field illustration

J. Delmerico, T. Cieslewski, H. Rebecq, M. Faessler, D. Scaramuzza

Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset

IEEE International Conference on Robotics and Automation (ICRA), 2019.

PDF YouTube Project Webpage and Datasets Code


Event-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes, called "events", instead of traditional video images. These asynchronous sensors naturally respond to motion in the scene with very low latency (in the order of microseconds) and have a very high dynamic range. These features, along with a very low power consumption, make event cameras an ideal sensor for fast robot localization and wearable applications, such as AR/VR and gaming. Considering these applications, we present a method to track the 6-DOF pose of an event camera in a known environment, which we contemplate to be described by a photometric 3D map (i.e., intensity plus depth information) built via classic dense 3D reconstruction algorithms. Our approach uses the raw events, directly, without intermediate features, within a maximum-likelihood framework to estimate the camera motion that best explains the events via a generative model. We successfully evaluate the method using both simulated and real data, and show improved results over the state of the art. We release the datasets and code to the public to foster reproducibility and research in this topic.


References

Pose tracking with an Event-based camera using non-linear optimization

S. Bryner, G. Gallego, H. Rebecq, D. Scaramuzza

Event-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization

IEEE International Conference on Robotics and Automation (ICRA), 2019.

PDF Poster YouTube Project Webpage, Datasets and Code


ESIM: an Open Event Camera Simulator

Event cameras measure changes of intensity asynchronously, in the form of a stream of events, which encode per-pixel brightness changes. In the last few years, their outstanding properties (asynchronous sensing, no motion blur, high dynamic range) have led to exciting vision applications, with very low-latency and high robustness. However, these sensors are still scarce and expensive to get, slowing down progress of the research community. To address these issues, there is a huge demand for cheap, high-quality synthetic, labeled event for algorithm prototyping, deep learning and algorithm benchmarking. The development of such a simulator, however, is not trivial since event cameras work fundamentally differently from frame-based cameras. We present the first event camera simulator that can generate a large amount of reliable event data. The key component of our simulator is a theoretically sound, adaptive rendering scheme that only samples frames when necessary, through a tight coupling between the rendering engine and the event simulator. We release ESIM as open source.


References

CORL18_Rebecq

H. Rebecq, D. Gehrig, D. Scaramuzza

ESIM: an Open Event Camera Simulator

Conference on Robot Learning (CoRL), Zurich, 2018.

PDF YouTube Project page


EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames

We present EKLT, a feature tracking method that leverages the complementarity of event cameras and standard cameras to track visual features with low latency. Event cameras are novel sensors that output pixel-level brightness changes, called "events". They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce different events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide low latency updates. In contrast to previous works, which are based on heuristics, this is the first principled method that uses raw intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are both more accurate (subpixel accuracy) and longer than the state of the art, across a wide variety of scenes.


References

EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames

D. Gehrig, H. Rebecq, G. Gallego, D. Scaramuzza

EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames

International Journal of Computer Vision (IJCV), 2019.

PDF YouTube Evaluation Code Tracking Code


Asynchronous, Photometric Feature Tracking using Events and Frames

D. Gehrig, H. Rebecq, G. Gallego, D. Scaramuzza

Asynchronous, Photometric Feature Tracking using Events and Frames

European Conference on Computer Vision (ECCV), Munich, 2018.

Oral Presentation.

PDF Poster YouTube Oral presentation Evaluation Code Tracking Code


Semi-Dense 3D Reconstruction with a Stereo Event Camera

Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. This paper presents a solution to the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping. The proposed method consists of the optimization of an energy function designed to exploit small-baseline spatio-temporal consistency of events triggered across both stereo image planes. To improve the density of the reconstruction and to reduce the uncertainty of the estimation, a probabilistic depth-fusion strategy is also developed. The resulting method has no special requirements on either the motion of the stereo event-camera rig or on prior knowledge about the scene. Experiments demonstrate our method can deal with both texture-rich scenes as well as sparse scenes, outperforming state-of-the-art stereo methods based on event data image representations.


References

Semi-Dense 3D Reconstruction with a Stereo Event Camera

Y. Zhou, G. Gallego, H. Rebecq, L. Kneip, H. Li, D. Scaramuzza

Semi-Dense 3D Reconstruction with a Stereo Event Camera

European Conference on Computer Vision (ECCV), Munich, 2018.

PDF Poster YouTube Project page and Data


Continuous-Time Visual-Inertial Odometry for Event Cameras

In this paper, we leverage a continuous-time framework to perform visual-inertial odometry with an event camera. This framework allows direct integration of the asynchronous events with micro-second accuracy and the inertial measurements at high frequency. The event camera trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines. This formulation significantly reduces the number of variables in trajectory estimation problems. We evaluate our method on real data from several scenes and compare the results against ground truth from a motion-capture system. We show that our method provides improved accuracy over the result of a state-of-the-art visual odometry method for event cameras. We also show that both the map orientation and scale can be recovered accurately by fusing events and inertial data. To the best of our knowledge, this is the first work on visual-inertial fusion with event cameras using a continuous-time framework.

References

TRO18_Mueggler

E. Mueggler, G. Gallego, H. Rebecq, D. Scaramuzza

Continuous-Time Visual-Inertial Odometry for Event Cameras

IEEE Transactions on Robotics, 2018.

PDF


A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation

We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the event data by maximizing an objective function: the contrast of an image of warped events. Our method implicitly handles data association between the events, and therefore, does not rely on additional appearance information about the scene. In addition to accurately recovering the motion parameters of the problem, our framework produces motion-corrected edge-like images with high dynamic range that can be used for further scene analysis. The proposed method is not only simple, but more importantly, it is, to the best of our knowledge, the first method that can be successfully applied to such a diverse set of important vision tasks with event cameras.


References

A Unifying Contrast Maximization Framework for Event Cameras

G. Gallego, H. Rebecq, D. Scaramuzza

A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth and Optical Flow Estimation

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, 2018.

Spotlight Presentation.

PDF Poster YouTube Spotlight presentation


Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.


References

Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

A.I. Maqueda, A. Loquercio, G. Gallego, N. Garcia, D. Scaramuzza

Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, 2018.

PDF Poster YouTube Code


Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

In this paper, we present the first state estimation pipeline that leverages the complementary advantages of a standard camera with an event camera by fusing in a tightly-coupled manner events, standard frames, and inertial measurements. We show on the Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement of 130% over event-only pipelines, and 85% over standard-frames only visual-inertial systems, while still being computationally tractable.

Furthermore, we use our pipeline to demonstrate - to the best of our knowledge - the first autonomous quadrotor flight using an event camera for state estimation, unlocking flight scenarios that were not reachable with traditional visual inertial odometry, such as low-light environments and high dynamic range scenes.


References

RAL18_VidalRebecq

A. Rosinol Vidal, H.Rebecq, T. Horstschaefer, D. Scaramuzza

Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios

IEEE Robotics and Automation Letters (RA-L), 2018.

PDF YouTube ICRA18 Video Pitch Poster Results (raw trajectories) Project Webpage


EMVS: Event-Based Multi-View Stereo - 3D Reconstruction with an Event Camera in Real-Time

IJCV video


BMVC video


We introduce the problem of event-based multi-view stereo (EMVS) for event cameras and propose a solution to it. Unlike traditional MVS methods, which address the problem of estimating dense 3D structure from a set of known viewpoints, EMVS estimates semi-dense 3D structure from an event camera with known trajectory. Our EMVS solution elegantly exploits two inherent properties of an event camera: (1) its ability to respond to scene edges - which naturally provide semi-dense geometric information without any preprocessing operation - and (2) the fact that it provides continuous measurements as the sensor moves. Despite its simplicity (it can be implemented in a few lines of code), our algorithm is able to produce accurate, semi-dense depth maps, without requiring any explicit data association or intensity estimation. We successfully validate our method on both synthetic and real data. Our method is computationally very efficient and runs in real-time on a CPU. We release the source code.

References

3D reconstruction with an Event-based camera in real-time

H. Rebecq, G. Gallego, E. Mueggler, D. Scaramuzza

EMVS: Event-Based Multi-View Stereo - 3D Reconstruction with an Event Camera in Real-Time

International Journal of Computer Vision, 2017.

PDF YouTube Source Code


EMVS paper

H. Rebecq, G. Gallego, D. Scaramuzza

EMVS: Event-based Multi-View Stereo

British Machine Vision Conference (BMVC), York, 2016.

Best Industry Related Paper (sponsored by nvidia and BMVA)

PDF PPT YouTube Source Code


Event-based, 6-DOF Camera Tracking from Photometric Depth Maps

We present an event-based approach for ego-motion estimation, which provides pose updates upon the arrival of each event, thus virtually eliminating latency. Our method is the first work addressing and demonstrating event-based pose tracking in six degrees-of-freedom (DOF) motions in realistic and natural scenes, and it is able to track high-speed motions. The method is successfully evaluated in both indoor and outdoor scenes.

References

Pose tracking with an Event-based camera

G. Gallego, Jon E. A. Lund, E. Mueggler, H. Rebecq, T. Delbruck, D. Scaramuzza

Event-based, 6-DOF Camera Tracking from Photometric Depth Maps

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

PDF Poster YouTube Datasets


Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization

We propose a novel, accurate tightly-coupled visual-inertial odometry pipeline for event cameras that leverages their outstanding properties to estimate the camera ego-motion in challenging conditions, such as high-speed motion or high dynamic range scenes. Our pipeline can output poses at a rate proportional to the camera velocity and runs in real-time on a CPU.

The method tracks a set of features (extracted on the image plane) through time. To achieve that, we consider events in overlapping spatio-temporal windows and align them using the current camera motion and scene structure, yielding motion-compensated event frames. We then combine these feature tracks in a keyframe-based, visual-inertial odometry algorithm based on nonlinear optimization to estimate the camera's 6-DOF pose, velocity, and IMU biases.

We evaluated the proposed method quantitatively on the public Event Camera Dataset and it significantly outperforms the state-of-the-art, while being computationally much more efficient: our pipeline can run much faster than real-time on a laptop and even on a smartphone processor. Furthermore, we demonstrate qualitatively the accuracy and robustness of our pipeline on a large-scale dataset, and an extremely high-speed dataset recorded by spinning an event camera on a leash at 850 deg/s.


References

BMVC17_Rebecq

H.Rebecq, T. Horstschaefer, D. Scaramuzza

Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization

British Machine Vision Conference (BMVC), London, 2017.

Oral Presentation. Acceptance Rate: 5.6%

PDF PPT YouTube Oral Presentation Results (raw trajectories)


Fast Event-based Corner Detection

Inspired by frame-based pre-processing techniques that reduce an image to a set of features, which are typically the input to higher-level algorithms, we propose a method to reduce an event stream to a corner event stream. Our goal is twofold: extract relevant tracking information (corners do not suffer from the aperture problem) and decrease the event rate for later processing stages. Our event-based corner detector is very efficient due to its design principle, which consists of working on the Surface of Active Events (a map with the timestamp of the latest event at each pixel) using only comparison operations. Our method asynchronously processes event by event with very low latency. Our implementation is capable of processing millions of events per second on a single core (less than a micro-second per event) and reduces the event rate by a factor of 10 to 20.

References

BMVC17_Mueggler

E. Mueggler, C. Bartolozzi, D. Scaramuzza

Fast Event-based Corner Detection

British Machine Vision Conference (BMVC), London, 2017.

PDF Poster YouTube Open-Source Code


EVO: Event-based, 6-DOF Parallel Tracking and Mapping in Real-Time

We present EVO, an Event-based Visual Odometry algorithm. Our algorithm successfully leverages the outstanding properties of event cameras to track fast camera motions while recovering a semi-dense 3D map of the environment. The implementation runs in real-time on a standard CPU and outputs up to several hundred pose estimates per second. Due to the nature of event cameras, our algorithm is unaffected by motion blur and operates very well in challenging, high dynamic range conditions with strong illumination changes. To achieve this, we combine a novel, event-based tracking approach based on image-to-model alignment with a recent event-based 3D reconstruction algorithm in a parallel fashion. Additionally, we show that the output of our pipeline can be used to reconstruct intensity images from the binary event stream, though our algorithm does not require such intensity information. We believe that this work makes significant progress in SLAM by unlocking the potential of event cameras. This allows us to tackle challenging scenarios that are currently inaccessible to standard cameras.

References

EVO

H. Rebecq, T. Horstschaefer, G. Gallego, D. Scaramuzza

EVO: A Geometric Approach to Event-based 6-DOF Parallel Tracking and Mapping in Real-time

IEEE Robotics and Automation Letters (RA-L), 2016.

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Accurate Angular Velocity Estimation with an Event Camera

We present an algorithm to estimate the rotational motion of an event camera. In contrast to traditional cameras, which produce images at a fixed rate, event cameras have independent pixels that respond asynchronously to brightness changes, with microsecond resolution. Our method leverages the type of information conveyed by these novel sensors (that is, edges) to directly estimate the angular velocity of the camera, without requiring optical flow or image intensity estimation. The core of the method is a contrast maximization design. The method performs favorably against round truth data and gyroscopic measurements from an Inertial Measurement Unit, even in the presence of very high-speed motions (close to 1000 deg/s).

References

EVO

G. Gallego and D. Scaramuzza

Accurate Angular Velocity Estimation with an Event Camera

IEEE Robotics and Automation Letters (RA-L), 2016.

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The Event Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM

We present the world's first collection of datasets with an event-based camera for high-speed robotics. The data also include intensity images, inertial measurements, and ground truth from a motion-capture system. An event-based camera is a revolutionary vision sensor with three key advantages: a measurement rate that is almost 1 million times faster than standard cameras, a latency of 1 microsecond, and a high dynamic range of 130 decibels (standard cameras only have 60 dB). These properties enable the design of a new class of algorithms for high-speed robotics, where standard cameras suffer from motion blur and high latency. All the data are released both as text files and binary (i.e., rosbag) files. Find out more on the dataset website!

References

DAVIS dataset paper

E. Mueggler, H. Rebecq, G. Gallego, T. Delbruck, D. Scaramuzza

The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM

International Journal of Robotics Research, Vol. 36, Issue 2, pages 142-149, Feb. 2017.

Dataset Page PDF YouTube


Low-Latency Visual Odometry using Event-based Feature Tracks

IROS'16 video


EBCCSP'16 video


We develop an event-based feature tracking algorithm for the DAVIS sensor and show how to integrate it in an event-based visual odometry pipeline. Features are first detected in the grayscale frames and then tracked asynchronously using the stream of events. The features are then fed to an event-based visual odometry pipeline that tightly interleaves robust pose optimization and probabilistic mapping. We show that our method successfully tracks the 6-DOF motion of the sensor in natural scenes (see video above).

References

Low-Latency Visual Odometry using Event-based Feature Tracks

B. Kueng, E. Mueggler, G. Gallego, D. Scaramuzza

Low-Latency Visual Odometry using Event-based Feature Tracks

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016.

Best Application Paper Award Finalist! Highlight Talk: Acceptance Rate 2.5%

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Feature Detection and Tracking with the Dynamic and Active-pixel Vision Sensor (DAVIS)

D. Tedaldi, G. Gallego, E. Mueggler, D. Scaramuzza

Feature Detection and Tracking with the Dynamic and Active-pixel Vision Sensor (DAVIS)

International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP), Krakow, 2016.

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ELiSeD

C. Braendli, J. Strubel, S. Keller, D. Scaramuzza, T. Delbruck

ELiSeD - An Event-Based Line Segment Detector

International Conference on Event-Based Control, Communication and Signal Processing (EBCCSP), Krakow, 2016.

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Continuous-Time Trajectory Estimation for Event-based Vision Sensors

In this paper, we address ego-motion estimation for an event-based vision sensor using a continuous-time framework to directly integrate the information conveyed by the sensor. The DVS pose trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines and it is optimized according to the observed events. We evaluate our method using datasets acquired from sensor-in-the-loop simulations and onboard a quadrotor performing flips. The results are compared to the ground truth, showing the good performance of the proposed technique.

References

RSS2015_Mueggler

E. Mueggler, G. Gallego, D. Scaramuzza

Continuous-Time Trajectory Estimation for Event-based Vision Sensors

Robotics: Science and Systems (RSS), Rome, 2015.

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Event-based Camera Pose Tracking using a Generative Event Model

We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.

References

arXiv15 paper

G. Gallego, C. Forster, E. Mueggler, D. Scaramuzza

Event-based Camera Pose Tracking using a Generative Event Model

arXiv:1510.01972, 2015.

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Lifetime Estimation of Events from Dynamic Vision Sensors

We develop an algorithm that augments each event with its "lifetime", which is computed from the event's velocity on the image plane. The generated stream of augmented events gives a continuous representation of events in time, hence enabling the design of new algorithms that outperform those based on the accumulation of events over fixed, artificially-chosen time intervals. A direct application of this augmented stream is the construction of sharp gradient (edge-like) images at any time instant. We successfully demonstrate our method in different scenarios, including high-speed quadrotor flips, and compare it to standard visualization methods.

References

ICRA2015_Mueggler

E. Mueggler, C. Forster, N. Baumli, G. Gallego, D. Scaramuzza

Lifetime Estimation of Events from Dynamic Vision Sensors

IEEE International Conference on Robotics and Automation (ICRA), Seattle, 2015.

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Event-based, 6-DOF Pose Tracking for High-Speed Maneuvers

In the last few years, we have witnessed impressive demonstrations of aggressive flights and acrobatics using quadrotors. However, those robots are actually blind. They do not see by themselves, but through the "eyes" of an external motion capture system. Flight maneuvers using onboard sensors are still slow compared to those attainable with motion capture systems. At the current state, the agility of a robot is limited by the latency of its perception pipeline. To obtain more agile robots, we need to use faster sensors. In this paper, we present the first onboard perception system for 6-DOF localization during high-speed maneuvers using a Dynamic Vision Sensor (DVS). Unlike a standard CMOS camera, a DVS does not wastefully send full image frames at a fixed frame rate. Conversely, similar to the human eye, it only transmits pixel-level brightness changes at the time they occur with microsecond resolution, thus, offering the possibility to create a perception pipeline whose latency is negligible compared to the dynamics of the robot. We exploit these characteristics to estimate the pose of a quadrotor with respect to a known pattern during high-speed maneuvers, such as flips, with rotational speeds up to 1,200 degrees a second. Additionally, we provide a versatile method to capture ground-truth data using a DVS.

References

IROS2014_Mueggler

E. Mueggler, B. Huber, D. Scaramuzza

Event-based, 6-DOF Pose Tracking for High-Speed Maneuvers

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, 2014.

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Low-Latency Event-Based Visual Odometry

This paper presents the first visual odometry system based on a DVS plus a normal CMOS camera to provide the absolute brightness values. The two sources of data are automatically spatiotemporally calibrated from logs taken during normal operation. We design a visual odometry method that uses the DVS events to estimate the relative displacement since the previous CMOS frame by processing each event individually. Experiments show that the rotation can be estimated with surprising accuracy, while the translation can be estimated only very noisily, because it produces few events due to very small apparent motion.

References

ICRA2014_Censi

A. Censi, D. Scaramuzza,

Low-Latency Event-Based Visual Odometry

IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014.

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Low-latency localization by Active LED Markers tracking using a DVS

This paper presents a method for low-latency pose tracking using a DVS and Active Led Markers (ALMs), which are LEDs blinking at high frequency (>1 KHz). The sensor's time resolution allows distinguishing different frequencies, thus avoiding the need for data association. This approach is compared to traditional pose tracking based on a CMOS camera. The DVS performance is not affected by fast motion, unlike the CMOS camera, which suffers from motion blur.

References

IROS2013_Censi

A. Censi, J. Strubel, C. Brandli, T. Delbruck, D. Scaramuzza,

Low-latency localization by Active LED Markers tracking using a Dynamic Vision Sensor

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, 2013.

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