Assembler：基于锚点扩散的可扩展三维部件装配
Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion
AI总结：
Assembler通过扩散模型和锚点云表示，将三维部件装配视为生成问题，构建32万级数据集，在PartNet上达到最先进性能，并支持从图像生成可编辑对象。
摘要：
我们提出了Assembler，一个可扩展且泛化能力强的三维部件装配框架，它能够从输入的部件网格和参考图像重建完整对象。与以往主要依赖确定性部件姿态预测和类别特定训练的方法不同，Assembler旨在处理具有不同部件数量、几何形状和结构的多样化、真实世界对象。它通过在任务表述、表示和数据方面的创新，解决了扩展到通用三维部件装配的核心挑战。首先，Assembler将部件装配视为一个生成问题，并采用扩散模型来采样合理的配置，有效捕捉由对称性、重复部件和多种有效装配方式引起的歧义。其次，我们引入了一种基于稀疏锚点云的新型形状中心表示，使得能够在欧几里得空间中进行可扩展生成，而非SE(3)姿态预测。第三，我们利用基于现有三维形状库的合成和过滤流程，构建了一个包含超过32万个多样化部件-对象装配的大规模数据集。Assembler在PartNet上达到了最先进的性能，并且是首个在复杂真实世界对象上展示高质量装配的方法。基于Assembler，我们进一步引入了一个有趣的部件感知三维建模系统，该系统从图像生成高分辨率、可编辑的对象，展示了交互式和组合设计的潜力。项目页面：https://assembler3d.github.io
英文摘要：
We present Assembler, a scalable and generalizable framework for 3D part assembly that reconstructs complete objects from input part meshes and a reference image. Unlike prior approaches that mostly rely on deterministic part pose prediction and category-specific training, Assembler is designed to handle diverse, in-the-wild objects with varying part counts, geometries, and structures. It addresses the core challenges of scaling to general 3D part assembly through innovations in task formulation, representation, and data. First, Assembler casts part assembly as a generative problem and employs diffusion models to sample plausible configurations, effectively capturing ambiguities arising from symmetry, repeated parts, and multiple valid assemblies. Second, we introduce a novel shape-centric representation based on sparse anchor point clouds, enabling scalable generation in Euclidean space rather than SE(3) pose prediction. Third, we construct a large-scale dataset of over 320K diverse part-object assemblies using a synthesis and filtering pipeline built on existing 3D shape repositories. Assembler achieves state-of-the-art performance on PartNet and is the first to demonstrate high-quality assembly for complex, real-world objects. Based on Assembler, we further introduce an interesting part-aware 3D modeling system that generates high-resolution, editable objects from images, demonstrating potential for interactive and compositional design. Project page: https://assembler3d.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
