Score-PA: Score-based 3D Part Assembly

Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu +1 more
9/8/2023
cs.CV

Official code: J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly (13 stars, MIT, PyTorch, last commit 2023-09-18).

Abstract

Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.

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Code Implementations(1)

J-F-Cheng/Score-PA_Score-based-3D-Part-AssemblyOfficial100%
PyTorchMIT

[BMVC 2023 (Oral)] Score-PA: Score-based 3D Part Assembly

131C++, CudaSep 8, 20233 years agoMIT
3d-computer-visiongraph-neural-networkspytorchpytorch-geometricscore-based-generative-modeling

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Cite this paper

@article{cheng2023scorepa,
  title  = {Score-PA: Score-based 3D Part Assembly},
  author = {Junfeng Cheng and Mingdong Wu and Ruiyuan Zhang and Guanqi Zhan and Chao Wu and Hao Dong},
  year   = {2023},
  eprint = {2309.04220},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.04220v1}
}

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