Revisiting Deformable Convolution for Depth Completion
Xinglong Sun Jean Ponce Yu-Xiong Wang
Depth completion, which aims to generate high-quality dense depth maps from
sparse depth maps, has attracted increasing attention in recent years. Previous
work usually employs RGB images as guidance, and introduces iterative spatial
propagation to refine estimated coarse depth maps. However, most of the
propagation refinement methods require several iterations and suffer from a
fixed receptive field, which may contain irrelevant and useless information
with very sparse input. In this paper, we address these two challenges
simultaneously by revisiting the idea of deformable convolution. We propose an
effective architecture that leverages deformable kernel convolution as a
single-pass refinement module, and empirically demonstrate its superiority. To
better understand the function of deformable convolution and exploit it for
depth completion, we further systematically investigate a variety of
representative strategies. Our study reveals that, different from prior work,
deformable convolution needs to be applied on an estimated depth map with a
relatively high density for better performance. We evaluate our model on the
large-scale KITTI dataset and achieve state-of-the-art level performance in
both accuracy and inference speed. Our code is available at
https://github.com/AlexSunNik/ReDC.