用扩散模型生成3D零件组装方案，支持复杂真实物体的自动拼装。
Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion
- 将零件组装建模为生成任务，用扩散模型采样合理布局。
- 在320K个合成数据上训练，对复杂物体组装效果领先。
- 适合交互设计、可编辑3D建模，适用于多样真实场景。
我们提出 Assembler，一个可扩展且通用的3D零件组装框架，能够从输入的零件网格和参考图像重建完整物体。与以往依赖确定性姿态预测和特定类别训练的方法不同，Assembler 能处理具有不同零件数量、几何形状和结构的多样化真实物体。其核心创新包括：首先，将零件组装视为生成问题，采用扩散模型采样合理配置，有效捕捉对称性、重复零件及多重合法组装带来的不确定性；其次，提出基于稀疏锚点云的形状中心表示，实现欧氏空间中的可扩展生成，而非SE(3)姿态预测；第三，基于现有3D形状库构建了包含超过320K个多样化零件-物体组合的大规模数据集。Assembler 在 PartNet 上达到当前最佳性能，并首次实现了复杂真实物体的高质量组装。基于此，我们进一步开发了一个零件感知的3D建模系统，仅需图像即可生成高分辨率、可编辑的物体，展示了交互式与组合式设计的潜力。
原文摘要 · Abstract (English)
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
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