Noise-Aware Unsupervised Deep Lidar-Stereo Fusion [article]

Xuelian Cheng, Yiran Zhong, Yuchao Dai, Pan Ji, Hongdong Li
2019 arXiv   pre-print
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps. By introducing a novel "Feedback Loop" to connect the network input with output, LidarStereoNet could tackle both noisy Lidar points and misalignment between sensors that have been ignored in existing Lidar-stereo fusion studies. Besides, we propose to incorporate a piecewise planar model into network learning to
more » ... urther constrain depths to conform to the underlying 3D geometry. Extensive quantitative and qualitative evaluations on both real and synthetic datasets demonstrate the superiority of our method, which outperforms state-of-the-art stereo matching, depth completion and Lidar-Stereo fusion approaches significantly.
arXiv:1904.03868v1 fatcat:iavi3y5kgvfupethq6oo474ce4