Gaussian Belief Propagation Network for Depth Completion

Depth completion aims to predict a dense depth map froma color image with sparse depth measurements. Although deep learningmethods have achieved state-of-the-art (SOTA), effectively handling thesparse and irregular nature of input depth data in deep networks remainsa significant challenge, often limiting performance, especially under highsparsity. To overcome this limitation, we introduce the Gaussian BeliefPropagation Network (GBPN), a novel hybrid framework synergisticallyintegrating deep learning with probabilistic graphical models for end-to-end depth completion. Specifically, a scene-specific Markov Random Field(MRF) is dynamically constructed by the Graphical Model ConstructionNetwork (GMCN), and then inferred via Gaussian Belief Propagation(GBP) to yield the dense depth distribution. Crucially, the GMCN learnsto construct not only the data-dependent potentials of MRF but also itsstructure by predicting adaptive non-local edges, enabling the capture ofcomplex, long-range spatial dependencies. Furthermore, we enhance GBPwith a serial & parallel message passing scheme, designed for effectiveinformation propagation, particularly from sparse measurements. Exten-sive experiments demonstrate that GBPN achieves SOTA performanceon the NYUv2 and KITTI benchmarks. Evaluations across varying spar-sity levels, sparsity patterns, and datasets highlight GBPN’s superiorperformance, notable robustness, and generalizable capability.