Abstract
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.
Abstract (translated)
最近在点云深度学习领域的进展已经催生了能够仅基于无序点集的原始几何信息，在大规模点云上实现高精度部件标注的模型。同时，人体解析领域专注于从图像中预测身体部位和衣物/配饰标签。本研究旨在通过使大规模人类网格的顶点级语义分割成为可能来弥合这两个领域的差距。为此，开发了一个用于Thuman2.1数据集的伪真实标记流程：首先将网格对齐到标准姿态，并从多个视角进行分割；然后将生成的点级别标签投影回原始网格，以产生每个点的伪地面实况注释。 随后，提出了一种新颖且内存高效的采样策略——基于填充曲线序列化的窗口式迭代最远点采样（FPS），有效降采大规模点云。接着使用PointTransformer进行纯几何分割，使人类网格的语义解析无需依赖纹理信息即可完成。实验结果证实了所提方法的有效性和准确性。
URL
https://arxiv.org/abs/2507.18655
