In this paper, we propose an end-to-end deep learning network named
3dDepthNet, which produces an accurate dense depth image from a single pair of
sparse LiDAR depth and color image for robotics and autonomous driving tasks.
Based on the dimensional nature of depth images, our network offers a novel
3D-to-2D coarse-to-fine dual densification design that is both accurate and
lightweight. Depth densification is first performed in 3D space via point cloud
completion, followed by a specially designed encoder-decoder structure that
utilizes the projected dense depth from 3D completion and the original RGB-D
images to perform 2D image completion. Experiments on the KITTI dataset show
our network achieves state-of-art accuracy while being more efficient. Ablation
and generalization tests prove that each module in our network has positive
influences on the final results, and furthermore, our network is resilient to
even sparser depth.

本文提出 3dDepthNet，采用新颖的 3D-to-2D 粗 - 细双重加密设计，通过点云补全和编码解码结构完成深度图像的生成，旨在用于机器人和自动驾驶等领域，实验表明其有效性和高效性。