跳到正文
研踪PaperTrail
← 研踪 PaperTrail

arXiv:2611.01960 · v1

Event-Based Optical Flow Constraint via Time-Sliced Plane Fitting

首次提交 2026-11-01 22:54
版本更新 2026-11-01 22:54
雷达观测 2026-11-04 23:43
arXiv ↗PDF ↗

摘要

Event-based optical flow estimation using spatiotemporal planar approximation inherently assumes constant velocity and tends to average out temporal velocity variations. In this paper, we propose a flow estimation method that extends this planar approximation along the temporal axis. The resulting event-based optical flow constraint allows existing variational optical flow formulations to model temporal motion variation. Specifically, we partition the space-time volume into sub-intervals and apply a two-step planar approximation to track object edges consistently. Moreover, a spatial compensation mechanism and a temporal regularization term are incorporated to compensate for edge displacements and temporally stabilize the flow estimation, respectively. The experimental results show that modeling temporal motion variation improves flow estimation accuracy over conventional methods.

作者备注:5 pages, 5 figures, 1 table. Accepted for publication at IEEE VCIP 2026 (2026 IEEE International Conference on Visual Communications and Image Processing). (c) 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

AI 论文解读

基于摘要 · 非全文审阅

此版本尚未生成 AI 解读。解读由后台任务生成并按论文版本保存,登录后共享已有结果。

版本差异

目前只观测到一个版本,无法比较变化。之后收录新版本时,会在这里对照标题、作者、分类和摘要。

观测到的版本

相邻记录可能跳过中间版本;首次发现 v2 及以上也会标为版本更新。

v1 · 2026-11-01 22:54 · 最新观测版本

Event-Based Optical Flow Constraint via Time-Sliced Plane Fitting

Event-based optical flow estimation using spatiotemporal planar approximation inherently assumes constant velocity and tends to average out temporal velocity variations. In this paper, we propose a flow estimation method that extends this planar approximation along the temporal axis. The resulting event-based optical flow constraint allows existing variational optical flow formulations to model temporal motion variation. Specifically, we partition the space-time volume into sub-intervals and apply a two-step planar approximation to track object edges consistently. Moreover, a spatial compensation mechanism and a temporal regularization term are incorporated to compensate for edge displacements and temporally stabilize the flow estimation, respectively. The experimental results show that modeling temporal motion variation improves flow estimation accuracy over conventional methods.

查看此版本解读 →查看此版本原文 ↗