Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics
It addresses the problem of evaluating skeletonization quality for researchers in robotics and shape analysis, but it is incremental as it focuses on metrics rather than new algorithms.
This work tackles the lack of quantitative evaluation for skeletonization algorithms by defining geometric metrics to score results in terms of topological similarity, boundedness, centeredness, and smoothness, and provides an open-source tool for community use.
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.