Live Demonstration: Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison

Taverni, Gemma; Moeys, Diederik Paul; Li, Chenghan; Delbrück, Tobi; Cavaco, Celso; Motsnyi, Vasyl; Bello, David San Segundo

doi:10.1109/iscas.2018.8351314

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Semi-supervised methods use the grayscale images from a colocated camera (e.g., DAVIS [56]) as a supervisory signal: images are warped using the flow predicted by the ANN and their photometric consistency is used as loss function [34], [45], [46]. While such supervisory signal is easier to obtain than real-world GT flow, it may suffer from the limitations of frame-based cameras (e.g., motion blur and low dynamic range), consequently affecting the trained ANNs.…”

Section: Event-based Optical Flow Estimationmentioning

confidence: 99%

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Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization

Shiba,

Klose,

Aoki

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Event cameras respond to scene dynamics and provide signals naturally suitable for motion estimation with advantages, such as high dynamic range. The emerging field of event-based vision motivates a revisit of fundamental computer vision tasks related to motion, such as optical flow and depth estimation. However, state-of-the-art event-based optical flow methods tend to originate in frame-based deep-learning methods, which require several adaptations (data conversion, loss function, etc.) as they have very different properties. We develop a principled method to extend the Contrast Maximization framework to estimate dense optical flow, depth, and ego-motion from events alone. The proposed method sensibly models the space-time properties of event data and tackles the event alignment problem. It designs the objective function to prevent overfitting, deals better with occlusions, and improves convergence using a multi-scale approach. With these key elements, our method ranks first among unsupervised methods on the MVSEC benchmark and is competitive on the DSEC benchmark. Moreover, it allows us to simultaneously estimate dense depth and ego-motion, exposes the limitations of current flow benchmarks, and produces remarkable results when it is transferred to unsupervised learning settings. Along with various downstream applications shown, we hope the proposed method becomes a cornerstone on event-based motion-related tasks. Code is available at https://github.com/tub-rip/event based optical flow

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Section: Event-based Optical Flow Estimationmentioning

confidence: 99%

“…Notice that the indoor sequences do not have IMOs, and the outdoor sequences do not include scenes with IMOs in the benchmark evaluation. The event camera has 346 ⇥ 260 pixel resolution [56]. In total, we evaluate on 63.5 million events spanning 265 seconds.…”

Section: Datasets Metrics and Hyper-parametersmentioning

confidence: 99%

Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization

Shiba,

Klose,

Aoki

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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EDFLOW: Event Driven Optical Flow Camera With Keypoint Detection and Adaptive Block Matching

Liu

Delbrück

2022

IEEE Trans. Circuits Syst. Video Technol.

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Analytical Review of Event-Based Camera Depth Estimation Methods and Systems

Furmonas

Liobe

Barzdėnas

2022

Sensors

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Event-based cameras have increasingly become more commonplace in the commercial space as the performance of these cameras has also continued to increase to the degree where they can exponentially outperform their frame-based counterparts in many applications. However, instantiations of event-based cameras for depth estimation are sparse. After a short introduction detailing the salient differences and features of an event-based camera compared to that of a traditional, frame-based one, this work summarizes the published event-based methods and systems known to date. An analytical review of these methods and systems is performed, justifying the conclusions drawn. This work is concluded with insights and recommendations for further development in the field of event-based camera depth estimation.

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Live Demonstration: Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison

Cited by 3 publications

References 2 publications

Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization

Secrets of Event-based Optical Flow, Depth and Ego-motion Estimation by Contrast Maximization

EDFLOW: Event Driven Optical Flow Camera With Keypoint Detection and Adaptive Block Matching

Analytical Review of Event-Based Camera Depth Estimation Methods and Systems

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