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SynBench: A Synthetic Benchmark for Non-rigid 3D Point Cloud Registration

arXiv 2409.14474 ↗First posted 22 Sep 2024cs.CV cs.AI cs.GRcited by 0

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Abstract (arXiv)

Non-rigid point cloud registration is a crucial task in computer vision. Evaluating a non-rigid point cloud registration method requires a dataset with challenges such as large deformation levels, noise, outliers, and incompleteness. Despite the existence of several datasets for deformable point cloud registration, the absence of a comprehensive benchmark with all challenges makes it difficult to achieve fair evaluations among different methods. This paper introduces SynBench, a new non-rigid point cloud registration dataset created using SimTool, a toolset for soft body simulation in Flex and Unreal Engine. SynBench provides the ground truth of corresponding points between two point sets and encompasses key registration challenges, including varying levels of deformation, noise, outliers, and incompleteness. To the best of the authors' knowledge, compared to existing datasets, SynBench possesses three particular characteristics: (1) it is the first benchmark that provides various challenges for non-rigid point cloud registration, (2) SynBench encompasses challenges of varying difficulty levels, and (3) it includes ground truth corresponding points both before and after deformation. The authors believe that SynBench enables future non-rigid point cloud registration methods to present a fair comparison of their achievements. SynBench is publicly available at: https://doi.org/10.11588/data/R9IKCF.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 96 references that are in the map. First posted on arXiv 22 Sep 2024.

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40 of 96 references.

References · 40

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28 more
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