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.
翻译:在本文中,我们从自我驾驶的角度处理场景流量估算问题。我们利用深层次的学习技巧和强大的前科,如同我们的应用领域一样,现场运动可以由机器人的运动和场面演员的三维运动组成。我们把问题发展成一个深层次结构化模型,通过解开高森-纽顿解答器,可以在GPU中有效地解决能源最小化问题。我们在具有挑战性的KITTI场景流数据集中的实验显示,我们比最新技术高出了800倍的距离,同时速度也快了800倍。