Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debris flows. We propose DEFLOW, a model for 3D motion estimation of debris flows, together with a newly captured dataset. We adopt a novel multi-level sensor fusion architecture and self-supervision to incorporate the inductive biases of the scene. We further adopt a multi-frame temporal processing module to enable flow speed estimation over time. Our model achieves state-of-the-art optical flow and depth estimation on our dataset, and fully automates the motion estimation for debris flows. The source code and dataset are available at project page.
@article{arxiv.2304.02569,
title = {DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow},
author = {Liyuan Zhu and Yuru Jia and Shengyu Huang and Nicholas Meyer and Andreas Wieser and Konrad Schindler and Jordan Aaron},
journal= {arXiv preprint arXiv:2304.02569},
year = {2023}
}
Comments
Photogrammetric Computer Vision Workshop, CVPRW 2023, camera ready