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:
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通过 Tri-Perspective view Decomposition (TPVD) 框架，该研究旨在解决自动驾驶中的深度完成任务，并且能够更准确地重建稀疏和嘈杂深度测量所对应的场景的精确 3D 几何形状。通过 TPVD 的分解和合并方法，与现有方法相比，在 KITTI、NYUv2 和 SUN RGBD 数据集上展现了更好的性能。