# Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics

arXiv: https://arxiv.org/abs/2504.00032 · HTML: https://mlsanity.com/paper/2504.00032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation](https://mlsanity.com/paper/2301.07525/) (2023)
- [Point2Skeleton: Learning Skeletal Representations from Point Clouds](https://mlsanity.com/paper/2012.00230/) (2020)
- [CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees](https://mlsanity.com/paper/2304.04708/) (2023)
- [Autonomous Cave Surveying with an Aerial Robot](https://mlsanity.com/paper/2003.13883/) (2020)
- [3D Skeletonization of Complex Grapevines for Robotic Pruning](https://mlsanity.com/paper/2307.11706/) (2023)
- [Planning High-Quality Grasps using Mean Curvature Object Skeletons](https://mlsanity.com/paper/1710.02418/) (2017)
- [Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies](https://mlsanity.com/paper/2301.08387/) (2023)
- [A Survey on Path Planning Problem of Rolling Contacts: Approaches, Applications and Future Challenges](https://mlsanity.com/paper/2501.04442/) (2025)

## Abstract (arXiv)

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.