骨格化品質評価：ロボティクスにおける点群解析のための幾何学的指標
Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics
点群形状の骨格化結果を、位相的類似性・有界性・中心性・滑らかさなどの幾何学的性質で定量的に評価する指標とスコアリング枠組みを提案し、オープンソースツールとして公開した。
著者: Qingmeng Wen, Yu-Kun Lai, Ze Ji, Seyed Amir Tafrishi
分類: cs.CV, cs.CG, cs.RO
原文アブストラクト
Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object's morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This work focuses on defining and quantifying geometric properties to systematically score the skeletonization results of point cloud shapes across multiple aspects, including topological similarity, boundedness, centeredness, and smoothness. We introduce these representative metric definitions along with a numerical scoring framework to analyze skeletonization outcomes concerning point cloud data for different scenarios, from object manipulation to mobile robot navigation. Additionally, we provide an open-source tool to enable the research community to evaluate and refine their skeleton models. Finally, we assess the performance and sensitivity of the proposed geometric evaluation methods from various robotic applications.
