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Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
[article]
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
arXiv:1904.03868v1
fatcat:iavi3y5kgvfupethq6oo474ce4