Self-Supervised Learning of Inlier Events for Event-based Optical Flow

Jun Nagata (Keio University),* Yoshimitsu Aoki (Keio University)
The 33rd British Machine Vision Conference


Event cameras asynchronously report per-pixel intensity changes with high temporal resolution. Their sparse and temporally precise nature is well suited for fast visual flow estimation. The normal optical flow can be estimated by fitting a plane to spatio-temporal events. However, least-squares plane fitting suffers from outliers due to the significant noise of events. Existing methods involve 1) iterative outlier rejection or 2) goodness-of-fit rejection after single-shot planar fitting from greedily selected spatially neighboring events. In contrast to these methods, we propose a method of selecting the events supporting a plane before performing a fitting, using the inlier probability from a lightweight neural network that captures both global and local structures. During inference, single-shot planar fitting is performed from only events with a higher inlier probability. We model each event selection by a Bernoulli distribution with the inlier probability and train the network to maximize the inlier count while sampling in a self-supervised manner. We verify that our event selection improves the accuracy of optical flow estimation with publicly available real data.



author    = {Jun Nagata and Yoshimitsu Aoki},
title     = {Self-Supervised Learning of Inlier Events for Event-based Optical Flow},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

Copyright © 2022 The British Machine Vision Association and Society for Pattern Recognition
The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The Association is a Company limited by guarantee, No.2543446, and a non-profit-making body, registered in England and Wales as Charity No.1002307 (Registered Office: Dept. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK).

Imprint | Data Protection