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HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion
[article]
2020
arXiv
pre-print
Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around the vehicle to perceive the surrounding environments. However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which aims at generating a dense depth map from an input sparse depth map. To effectively utilize multi-scale
arXiv:1808.08685v2
fatcat:pckzqe5oszcvtenafktze7prvi