Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image. In this work, we approach this problem by addressing two issues that have been under-researched in the open literature: sampling strategy (data term) and graph construction (prior term). First, instead of the popular random sampling strategy, we suggest that Poisson disk sampling is a much more effective solution to create sparse depth map from a dense version. We experimentally compare a class of quasi-random sampling strategies and demonstrate that an optimized sampling strategy can significantly improve the performance of depth completion for the same number of sparse samples. Second, instead of the traditional square kernel, we suggest that dynamic construction of local neighborhood is a better choice for interpolating the missing values. More specifically, we proposed an end-to-end network with a graph convolution module. Since the neighborhood relationship of 3D points is more effectively exploited by our novel graph convolution module, our approach has achieved not only state-of-the-art results for depth completion of indoor scenes but also better generalization ability than other competing methods. "