-
Live Demonstration: Unsupervised Event-Based Learning of Optical Flow, Depth and Egomotion
AbstractWe propose a demo of our work, Unsupervised Event-based Learning of Optical Flow, Depth and Egomotion, which will also appear at CVPR 2019. Our demo consists of a CNN which takes as input events from a DAVIS-346b event camera, represented as a discretized event volume, and predicts optical flow for each pixel in the image. Due to the generalization abilities of our network, we are able to predict accurate optical flow for a very wide range of scenes, including for very fast motions and challenging lighting.
Related Material
[pdf][bibtex]@InProceedings{Zhu_2019_CVPR_Workshops,
author = {Zihao Zhu, Alex and Yuan, Liangzhe and Chaney, Kenneth and Daniilidis, Kostas},
title = {Live Demonstration: Unsupervised Event-Based Learning of Optical Flow, Depth and Egomotion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
These CVPR 2019 workshop papers are the Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
This material is presented to ensure timely dissemination of scholarly and technical work.
Copyright and all rights therein are retained by authors or by other copyright holders.
All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.