GeoDC: Geometry-Constrained Depth Completion With Depth Distribution Modeling

Depth completion is a fundamental, yet not well-solved problem in 3-D vision. Current wisdom attempts to employ implicit geometric spatial cues from point clouds to assist in depth completion. However, these methods encounter challenges in extracting rich geometric features due to the absence of explicit constraints. In this article, we propose GeoDC, a geometry-constrained depth completion network with depth distribution modeling. GeoDC employs point cloud upsampling as an auxiliary task to guide the network in learning more robust and effective geometric features. Simultaneously, a novel image and point cloud fusion module, denoted as IP-Interaction, is implemented to holistically integrate features from images and point clouds. Besides, recognizing the presence of uncertainty and ambiguity in the ground-truth (GT) data, we construct a prior network and a posterior network to model depth feature distributions and leverage the distributions to guide depth map inference. GeoDC can solve both the problems of geometric constraint inadequacies in feature extraction and data uncertainty within depth maps well. Extensive experiments underscore the efficacy of our method, demonstrating comparable or superior performance when compared to existing state-of-the-art methods.