用形态学骨架指导点云采样，提升分类与检索精度
MorphoSkel3D: Morphological Skeletonization of 3D Point Clouds for Informed Sampling in Object Classification and Retrieval
- 基于形态学构建3D点云骨架，规则驱动无须训练
- 在ModelNet和ShapeNet上验证，不同采样率下均更准确
- 适合需要结构感知采样的点云任务，如分类与检索
点云是表示物体三维几何结构的数据点集合。点云处理的关键步骤是选取能代表形状的点子集。传统采样方法常忽略几何信息，而基于学习的采样模型虽表现优异，但融入几何先验可进一步增强对底层结构的学习与保持能力。为揭示形状本质，定性骨架可作为有效描述符，指导局部与全局几何的采样。本文提出MorphoSkel3D，一种基于形态学的3D点云骨架化新方法。该方法计算成本低，为规则驱动算法，可在ModelNet和ShapeNet两个大规模数据集上，于不同采样比例下进行质量与性能基准测试。实验表明，使用MorphoSkel3D进行训练，能实现更知情、更精确的采样，在物体分类与点云检索的实际应用中表现更优。
原文摘要 · Abstract (English)
Point clouds are a set of data points in space to represent the 3D geometry of objects. A fundamental step in the processing is to identify a subset of points to represent the shape. While traditional sampling methods often ignore to incorporate geometrical information, recent developments in learning-based sampling models have achieved significant levels of performance. With the integration of geometrical priors, the ability to learn and preserve the underlying structure can be enhanced when sampling. To shed light into the shape, a qualitative skeleton serves as an effective descriptor to guide sampling for both local and global geometries. In this paper, we introduce MorphoSkel3D as a new technique based on morphology to facilitate an efficient skeletonization of shapes. With its low computational cost, MorphoSkel3D is a unique, rule-based algorithm to benchmark its quality and performance on two large datasets, ModelNet and ShapeNet, under different sampling ratios. The results show that training with MorphoSkel3D leads to an informed and more accurate sampling in the practical application of object classification and point cloud retrieval.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。