CDAF: Cross-Modal and Dual-channel Upsample Adaptive Fusion network for Point Cloud Completion

In real-world scenarios, point cloud data often suffers from incompleteness due to limitations in sensor viewpoints, resolution constraints, and self-occlusions, which hinders its applications in domains such as autonomous driving and robotics. To address these challenges, this paper proposes a novel Cross-Modal and Dual-channel Upsample Adaptive Fusion network (CDAF), Our framework innovatively integrates depth maps with point clouds through dual-channel attention and gating units, significantly improving completion accuracy and detail recovery. The framework comprises two core modules: Cross-Modal Feature Enhancement (CMFE) and Dual-channel Upsampling Adaptive Fusion (DUAF). CMFE enhances point cloud feature representation by leveraging Spatial-activated Channel Attention to model channel-wise dependencies and Max-Sigmoid Attention to align cross-modal features between depth maps and point clouds, DUAF progressively refines coarse point clouds through a parallel structural analysis and similarity alignment branches, enabling adaptive fusion of local geometric priors and global shape consistency. Experimental results on multiple benchmark datasets demonstrate that CDAF surpasses existing state-of-the-art methods in point cloud completion tasks, showcasing superior global shape understanding and detail recovery.