Deep Rigid Instance Scene Flow

Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, Raquel Urtasun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3614-3622

Abstract


In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. We formulate the problem as energy minimization in a deep structured model, which can be solved efficiently in the GPU by unrolling a Gaussian-Newton solver. Our experiments in the challenging KITTI scene flow dataset show that we outperform the state-of-the-art by a very large margin, while being 800 times faster.

Related Material


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[bibtex]
@InProceedings{Ma_2019_CVPR,
author = {Ma, Wei-Chiu and Wang, Shenlong and Hu, Rui and Xiong, Yuwen and Urtasun, Raquel},
title = {Deep Rigid Instance Scene Flow},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}