EPCS：基于端点的低质量点云曲线骨架提取
EPCS: Endpoint-based Part-aware Curve Skeleton Extraction for Low-quality Point Clouds
摘要：
曲线骨架是一种重要的形状描述符，在计算机图形学、机器视觉和人工智能中有着广泛的应用。针对低质量点云数据，提出了一种基于端点的零件感知曲线骨架提取方法。首次提出了一种新的随机中心移动（RCS）方法来检测点云上的端点。该方法以端点为初始种子点进行分层，通过计算各层的定向限界框（OBB）的中心点得到骨架点。随后，骨架点被连接，从而形成分支。此外，还提出了多矢量动量驱动（MVMD）方法来定位连接分支的连接点。由于点云上不同部位之间的形状差异，通过去除多余的连接点，使用所提出的MVMD方法重新连接一些分支，并应用基于分裂算子的插值方法，最终优化骨架的全局拓扑结构。从而得到完整光滑的曲线骨架。与现有的几种方法进行了比较，实验结果验证了该方法的鲁棒性、有效性和高效性。此外，在破碎兵马俑点云上的骨架提取和模型分割结果也突出了该方法的实用性。
英文摘要：
The curve skeleton is an important shape descriptor that has been utilized in various applications in computer graphics, machine vision, and artificial intelligence. In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed. The novel random center shift (RCS) method is first proposed for detecting the endpoints on point clouds. The endpoints are used as the initial seed points for dividing each part into layers, and then the skeletal points are obtained by computing the center points of the oriented bounding box (OBB) of the layers. Subsequently, the skeletal points are connected, thus forming the branches. Furthermore, the multi-vector momentum-driven (MVMD) method is also proposed for locating the junction points that connect the branches. Due to the shape differences between different parts on point clouds, the global topology of the skeleton is finally optimized by removing the redundant junction points, re-connecting some branches using the proposed MVMD method, and applying an interpolation method based on the splitting operator. Consequently, a complete and smooth curve skeleton is achieved. The proposed EPCS method is compared with several state-of-the-art methods, and the experimental results verify its robustness, effectiveness, and efficiency. Furthermore, the skeleton extraction and model segmentation results on the point clouds of broken Terracotta also highlight the utility of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
