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.

翻译：自主三维零件装配是机器人学和三维计算机视觉领域中的一项挑战性任务。该任务旨在无需依赖预定义指令的情况下，将单个零件组装成完整形状。本文从全新的生成式视角出发，提出基于分数的三维零件装配框架（Score-PA）来解决该问题。针对基于分数的方法在推理阶段通常耗时较长这一缺陷，我们引入了一种名为快速预测-校正采样器（FPC）的新型算法，以加速框架内的采样过程。我们采用多种指标评估装配质量与多样性，评估结果表明我们的算法优于现有最先进方法。我们已在 https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly 开源代码。