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
Abstract
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.Video
Citation
@inproceedings{Nagata_2022_BMVC,
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 = {https://bmvc2022.mpi-inf.mpg.de/0785.pdf}
}
Copyright © 2022
The British Machine Vision Conference is organised by