Non-Local Spatial Propagation Network for Depth Completion

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixed-local neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Depth Completion KITTI Depth Completion NLSPN iRMSE 1.99 # 1
iMAE 0.84 # 1
RMSE 741.68 # 4
MAE 199.59 # 1
Runtime [ms] 220 # 11
Stereo-LiDAR Fusion KITTI Depth Completion Validation NLSPN RMSE 771.8 # 5
Depth Completion NYU-Depth V2 NLSPN RMSE 0.092 # 1
REL 0.012 # 1
Depth Completion VOID NLSPN MAE 26.736 # 1
RMSE 79.121 # 1
iMAE 12.703 # 1
iRMSE 33.876 # 1


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