DBFNM: Dual-Branch Fusion Network With Mamba Decoder for Indoor Depth Completion

Accurate depth maps are essential for indoor navigation and modeling by robots, but raw depth maps often have missing areas due to sensor limitations, environmental factors, and distance constraints. Existing methods that fuse RGB images with depth maps usually cannot utilize spatial structural information and exhibit poor accuracy at object edges. To bridge this gap, a dual-branch fusion network with Mamba decoder, called DBFNM, is proposed for depth completion in this work. It consists of two complementary branches: one branch utilizes semantic and texture information from RGB images as visual guidance, while the other extracts spatial geometric structures from normal maps as structural guidance. In particular, a geometric gated encoder is utilized to fully leverage spatial information. In the dual-branch decoding stage, a dual-branch feature interaction alignment module is designed, which is composed of three components, including dual-branch edge feature alignment, dual-branch interaction, and global alignment. Then, the decoded dual-branch features are processed by a dual-modal fusion network based on a spatial propagation network to obtain dense depth map predictions. Extensive experimental results on the NYU-Depth V2 and SUN RGB-D datasets demonstrate that DBF achieves superior depth completion performance compared to existing methods in indoor scenes, particularly in handling large-scale missing depth regions and preserving edge details.