Score-PA: Score-based 3D Part Assembly

Junfeng Cheng (Imperial College London), Mingdong Wu (Peking University), Ruiyuan Zhang (zhejiang university), Guanqi Zhan (University of Oxford), Chao Wu (Zhejiang University), Hao Dong (Peking University)*
The 34th British Machine Vision Conference


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. 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



author    = {Junfeng Cheng and Mingdong Wu and Ruiyuan Zhang and Guanqi Zhan and Chao Wu and Hao Dong},
title     = {Score-PA: Score-based 3D Part Assembly},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year      = {2023},
url       = {}

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