  Depth completion is a vital task for autonomous driving, as it involves
reconstructing the precise 3D geometry of a scene from sparse and noisy depth
measurements. However, most existing methods either rely only on 2D depth
representations or directly incorporate raw 3D point clouds for compensation,
which are still insufficient to capture the fine-grained 3D geometry of the
scene. To address this challenge, we introduce Tri-Perspective view
Decomposition (TPVD), a novel framework that can explicitly model 3D geometry.
In particular, (1) TPVD ingeniously decomposes the original point cloud into
three 2D views, one of which corresponds to the sparse depth input. (2) We
design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D
aggregation, where a Distance-Aware Spherical Convolution (DASC) is applied.
(3) By adaptively choosing TPV affinitive neighbors, the newly proposed
Geometric Spatial Propagation Network (GSPN) further improves the geometric
consistency. As a result, our TPVD outperforms existing methods on KITTI,
NYUv2, and SUN RGBD. Furthermore, we build a novel depth completion dataset
named TOFDC, which is acquired by the time-of-flight (TOF) sensor and the color
camera on smartphones. Project page:
https://yanzq95.github.io/projectpage/TOFDC/index.html
