Dual-Constraint Contrastive Completion: Bridging Local Precision and Global Integrity in 3D Point Cloud Reconstruction

Incomplete 3D point clouds present a significant challenge in diverse applications due to sensor limitations and occlusions. Existing methods often struggle to balance local detail accuracy and global structural integrity, frequently yielding artifacts or distortions due to reliance on symmetric Chamfer Distance or unguided contrastive losses. We propose Dual-Constraint Contrastive Completion (DCCC), an end-to-end framework integrating asymmetric weighted Chamfer distance with multi-granularity contrastive learning. DCCC utilizes an encoder-decoder backbone with Mamba layers for efficient feature extraction. Central is the Asymmetric Contrastive Chamfer Loss (ACCL), decoupling local precision and global integrity objectives into distinct contrastive components, optimized via dynamic asymmetric weighting. A Self-Supervised Structural Guidance (SSG) module further learns coarse structural priors directly from incomplete inputs, reducing annotation reliance and improving robustness. Extensive experiments on benchmark datasets demonstrate DCCC's superior performance. DCCC achieves best-in-class results across critical metrics, significantly enhancing structural completeness and fine-grained accuracy in diverse settings, including real-world scenarios and high sparsity.