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
this https URL

深度补全是从稀疏深度图生成高质量密集深度图的方法，本文通过重新思考可变形卷积的概念，提出了一个有效的架构，利用可变形核卷积作为单次细化模块，并在大规模 KITTI 数据集上取得了精度和推理速度方面的最新水平。