Deep Rigid Instance Scene Flow
It addresses scene flow estimation for self-driving applications, representing an incremental improvement with specific performance gains.
The paper tackles scene flow estimation for self-driving by formulating it as energy minimization in a deep structured model, leveraging deep learning and motion priors. It outperforms state-of-the-art by a large margin on the KITTI dataset and is 800 times faster.
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.