Non-rigid registration is essential for reconstructing dynamic and incomplete 3D human meshes, yet traditional methods often fail to achieve robust alignment in the sequence of high-motion deformations and missing geometry. We propose a domain crossover non-rigid registration (DCNRR) framework that addresses these challenges by effectively transferring informative features from 2D image space into the 3D mesh domain of three key stages: multi-view projection, hierarchical non-rigid registration, and topology-consistent completion. In the first stage, multi-view projections are used to extract 2D joint locations and deep features, which guide deformation in the 3D space. In the second stage, hierarchical joint priors and deep features collaboratively guide mesh alignment, enabling more accurate deformation in distal regions and complex poses. In the final stage, we apply a diffusion-based completion process in UV coordinates to reconstruct incomplete surface normals and refine missing mesh areas with topological consistency. Our approach achieves highly detailed and perceptually accurate mesh deformation. To validate our approach, we evaluate performance on a newly constructed dynamic human motion (DHM) dataset, as well as public datasets. Our method demonstrates state-of-the-art results in both geometric accuracy and stability, showing particular robustness in dynamic and incomplete mesh sequences.

Recent learning-based multi-view stereo (MVS) still exhibits insufficient accuracy in large occlusion cases, such as environments with significant inter-camera distance or when capturing objects with complex shapes. This is because incorrect image features extracted from occluded areas serve as significant noise in the cost volume construction. To address this, we propose a visibility-aware MVS using surface normal weighting (SnowMVSNet) based on explicit 3D geometry. It selectively suppresses mismatched features in the cost volume construction by computing inter-view visibility. Additionally, we present a geometry-guided cost volume regularization that enhances true depth among depth hypotheses using a surface normal prior. We also propose intra-view visibility that distinguishes geometrically more visible pixels within a reference view. Using intra-view visibility, we introduce the visibility-weighted training and depth estimation methods. These methods enable the network to achieve accurate 3D point cloud reconstruction by focusing on visible regions. Based on simple inter-view and intra-view visibility computations, SnowMVSNet accomplishes substantial performance improvements relative to computational complexity, particularly in terms of occlusion robustness. To evaluate occlusion robustness, we constructed a multi-view human (MVHuman) dataset containing general human body shapes prone to self-occlusion. Extensive experiments demonstrated that SnowMVSNet significantly outperformed state-of-the-art methods in both low- and high-occlusion scenarios.