Deep Rigid Instance Scene Flow
| Autor: | Yuwen Xiong, Raquel Urtasun, Wei-Chiu Ma, Rui Hu, Shenlong Wang |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
050210 logistics & transportation business.industry Computer science Deep learning Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Solver Computer Science - Robotics Computer Science::Graphics 0502 economics and business 0202 electrical engineering electronic engineering information engineering Robot Leverage (statistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Robotics (cs.RO) ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.1904.08913 |
| Popis: | 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. Comment: CVPR 2019. Rank 1st on KITTI scene flow benchmark. 800 times faster than prior art |
| Databáze: | OpenAIRE |
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