Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Authors: Zhiqiang Yan, Yuankai Lin, Kun Wang, Yupeng Zheng, Yufei Wang, Zhenyu Zhang, Jun Li, Jian Yang

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.