NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image-Guided Depth Completion

Abstract

Depth images captured by low-cost three-dimensional (3D) cameras are subject to low spatial density, requiring depth completion to improve 3D imaging quality. Image-guided depth completion aims at predicting dense depth images from extremely sparse depth measurements captured by depth sensors with the guidance of aligned Red–Green–Blue (RGB) images. Recent approaches have achieved a remarkable improvement, but the performance will degrade severely due to the corruption in input sparse depth. To enhance robustness to input corruption, we propose a novel depth completion scheme based on a normalized spatial-variant diffusion network incorporating measurement uncertainty, which introduces the following contributions. First, we design a normalized spatial-variant diffusion (NSVD) scheme to apply spatially varying filters iteratively on the sparse depth conditioned on its certainty measure for excluding depth corruption in the diffusion. In addition, we integrate the NSVD module into the network design to enable end-to-end training of filter kernels and depth reliability, which further improves the structural detail preservation via the guidance of RGB semantic features. Furthermore, we apply the NSVD module hierarchically at multiple scales, which ensures global smoothness while preserving visually salient details. The experimental results validate the advantages of the proposed network over existing approaches with enhanced performance and noise robustness for depth completion in real-use scenarios.