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

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Qingmeng Wen , Yu‐Kun Lai , Ze Ji , Seyed Amir Tafrishi ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2504.00032

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.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2504.00032
PDF: https://arxiv.org/pdf/2504.00032
OA Status: green
OpenAlex ID: https://openalex.org/W4417222493

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OpenAlex ID: https://openalex.org/W4417222493

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DOI: https://doi.org/10.48550/arxiv.2504.00032

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

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-03-29

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Authors: Qingmeng Wen, Yu‐Kun Lai, Ze Ji, Seyed Amir Tafrishi

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Landing page: https://arxiv.org/abs/2504.00032

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PDF URL: https://arxiv.org/pdf/2504.00032

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2504.00032

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Cited by: 0

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