Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera
Urbano Miguel Nunes, Laurent Udo Perrinet, Sio-Hoi Ieng
摘要
Event cameras asynchronously report brightness changes with a temporal resolution in the order of microseconds, which makes them inherently suitable to address problems that involve rapid motion perception. In this paper, we address the problem of time-to-contact (TTC) estimation using a single event camera. This problem is typically addressed by estimating a single global TTC measure, which explicitly assumes that the surface/obstacle is planar and fronto-parallel. We relax this assumption by proposing an incremental event-based method to estimate the TTC that jointly estimates the (up-to scale) inverse depth and global motion using a single event camera. The proposed method is reliable and fast while asynchronously maintaining a TTC map (TTCM), which provides per-pixel TTC estimates. As a side product, the proposed method can also estimate per-event optical flow. We achieve state-of-the-art performances on TTC estimation in terms of accuracy and runtime per event while achieving competitive performance on optical flow estimation.
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- End-to-End Learning of Representations for Asynchronous Event-Based DataDaniel Gehrig, Antonio Loquercio, Konstantinos G. Derpanis, Davide ScaramuzzaICCV 2019 · 被引用 427 次
- Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural NetworksJesse J. Hagenaars, Federico Paredes-Vallés, Guido de CroonNeurIPS 2021 · 被引用 178 次
- Event-Based Motion Segmentation by Motion CompensationTimo Stoffregen, Guillermo Gallego, Tom Drummond, Lindsay Kleeman 等ICCV 2019 · 被引用 164 次
- Spatio-Temporal Recurrent Networks for Event-Based Optical Flow EstimationZiluo Ding, Rui Zhao, Jiyuan Zhang, Tianxiao Gao 等AAAI 2022 · 被引用 76 次
- Adaptive Global Decay Process for Event CamerasUrbano Miguel Nunes, Ryad Benosman, Sio-Hoi IengCVPR 2023
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