computer-vision-8

Event-based Motion Estimation

3260 papers • 126 benchmarks • 313 datasets

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uzh-rpg/event-based_vision_resources

2 papers 2,631

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COESOT

FE108

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Most implemented papers

Globally Optimal Contrast Maximisation for Event-Based Motion Estimation

Tat-Jun Chin, Daqi Liu, Álvaro Parra Bustos•Mon Feb 24 2020

This work proposes a new globally optimal event-based motion estimation algorithm based on branch-and-bound (BnB), which solves rotational motion estimation on event streams, which supports practical applications such as video stabilisation and attitude estimation.

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Event-based Star Tracking via Multiresolution Progressive Hough Transforms

Tat-Jun Chin, Samya Bagchi•Tue Jun 18 2019

This work proposes an event-based processing approach for star tracking, which operates on the event stream from a star field, by using multi-resolution Hough Transforms to time-progressively integrate event data and produce accurate relative rotations.

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Event Collapse in Contrast Maximization Frameworks

Guillermo Gallego, Y. Aoki, Shintaro Shiba•Thu Jun 30 2022

This work demonstrates event collapse in its simplest form and proposes collapse metrics by using first principles of space–time deformation based on differential geometry and physics, which are the only effective solution against event collapse against well-posed warps in the experimental settings considered.

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A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework

Guillermo Gallego, Y. Aoki, Shintaro Shiba•Tue Dec 13 2022

A novel, computationally efficient regularizer based on geometric principles to mitigate event collapse is proposed and is hoped that this work opens the door for future applications that unlocks the advantages of event cameras.

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Progressive Spatio-temporal Alignment for Efficient Event-based Motion Estimation

Zhiwei Xiong, Yueyi Zhang, Xueyan Huang•Wed May 31 2023

In this paper, we propose an efficient event-based motion estimation framework for various motion models. Different from previous works, we design a progressive event-to-map alignment scheme and utilize the spatio-temporal correlations to align events. In detail, we progressively align sampled events in an event batch to the time-surface map and obtain the updated motion model by minimizing a novel time-surface loss. In addition, a dynamic batch size strategy is applied to adaptively adjust the batch size so that all events in the batch are consistent with the current motion model. Our framework has three advantages: a) the progressive scheme refines motion parameters iteratively, achieving accurate motion estimation; b) within one iteration, only a small portion of events are involved in optimization, which greatly reduces the total runtime; c) the dynamic batch size strategy ensures that the constant velocity assumption always holds. We conduct comprehensive experiments to evaluate our framework on challenging high-speed scenes with three motion models: rotational, homography, and 6-DOF models. Experimental results demonstrate that our framework achieves state-of-the-art estimation accuracy and efficiency. The code is available at https://github.com/huangxueyan/PEME.

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Event-Based Visual Odometry on Non-Holonomic Ground Vehicles

L. Kneip, Wanting Xu, Si'ao Zhang, Li Cui, Xin-Zhong Peng•Tue Jan 16 2024

This work demonstrates reliable, purely event-based visual odometry on planar ground vehicles by employing the constrained non-holonomic motion model of Ackermann steering platforms, and extends single feature n-linearities for regular frame-based cameras to the case of quasi time-continuous event-tracks, and achieves a polynomial form via variable degree Taylor expansions.

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