Depth completion, aiming to predict dense depth maps from sparse depth
measurements, plays a crucial role in many computer vision related
applications. Deep learning approaches have demonstrated overwhelming success
in this task. However, high-precision depth completion without relying on the
ground-truth data, which are usually costly, still remains challenging. The
reason lies on the ignorance of 3D structural information in most previous
unsupervised solutions, causing inaccurate spatial propagation and mixed-depth
problems. To alleviate the above challenges, this paper explores the
utilization of 3D perceptual features and multi-view geometry consistency to
devise a high-precision self-supervised depth completion method. Firstly, a 3D
perceptual spatial propagation algorithm is constructed with a point cloud
representation and an attention weighting mechanism to capture more reasonable
and favorable neighboring features during the iterative depth propagation
process. Secondly, the multi-view geometric constraints between adjacent views
are explicitly incorporated to guide the optimization of the whole depth
completion model in a self-supervised manner. Extensive experiments on
benchmark datasets of NYU-Depthv2 and VOID demonstrate that the proposed model
achieves the state-of-the-art depth completion performance compared with other
unsupervised methods, and competitive performance compared with previous
supervised methods.

本研究提出了一种基于 3D 感知特征和多视角几何一致性的高精度自监督深度完成方法，通过构建 3D 感知空间传播算法和引入多视角几何约束，实现了无监督下的高精度深度完成，并在 NYU-Depthv2 和 VOID 等评测数据集上取得与有监督方法相媲美的性能。