用多视角图像生成人体网格语义分割标签，不依赖纹理信息
Part Segmentation of Human Meshes via Multi-View Human Parsing
- 通过多视角解析人体网格并回投影生成伪真值标签
- 提出窗口化迭代最远点采样，高效压缩点云减少内存占用
- 仅用几何信息实现高精度人体部件分割，适合无纹理数据场景
近期点云深度学习进展使得模型仅基于无序点集的原始几何信息，即可在大规模点云上实现高精度的逐部件标注。与此同时，人体解析领域致力于从图像中预测身体部位及服装/配饰标签。本文旨在融合这两个方向，实现大规模人体网格的逐顶点语义分割。为此，针对Thuman2.1数据集构建了伪真值标注流程：先将网格对齐至标准姿态，从多个视角进行分割，再将得到的点级标签回投影到原始网格，生成逐点伪真值注释。随后，提出一种新型内存高效的采样策略——基于空间填充曲线序列化的窗口化迭代最远点采样（windowed iterative FPS），有效降低点云规模。最终采用纯几何方法的PointTransformer进行分割，无需依赖纹理信息。实验验证了该方法的有效性与准确性。项目代码与预处理数据见https://github.com/JamesMcCullochDickens/Human3DParsing/tree/master。
原文摘要 · Abstract (English)
Recent advances in point cloud deep learning have led to models that achieve high per-part labeling accuracy on large-scale point clouds, using only the raw geometry of unordered point sets. In parallel, the field of human parsing focuses on predicting body part and clothing/accessory labels from images. This work aims to bridge these two domains by enabling per-vertex semantic segmentation of large-scale human meshes. To achieve this, a pseudo-ground truth labeling pipeline is developed for the Thuman2.1 dataset: meshes are first aligned to a canonical pose, segmented from multiple viewpoints, and the resulting point-level labels are then backprojected onto the original mesh to produce per-point pseudo ground truth annotations. Subsequently, a novel, memory-efficient sampling strategy is introduced, a windowed iterative farthest point sampling (FPS) with space-filling curve-based serialization to effectively downsample the point clouds. This is followed by a purely geometric segmentation using PointTransformer, enabling semantic parsing of human meshes without relying on texture information. Experimental results confirm the effectiveness and accuracy of the proposed approach. Project code and pre-processed data is available at https://github.com/JamesMcCullochDickens/Human3DParsing/tree/master.
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