PartRAG：基于检索的分层3D生成与编辑
PartRAG: Retrieval-Augmented Part-Level 3D Generation and Editing
AI总结：
PartRAG通过整合外部部分数据库和扩散变换器，实现基于检索的分层3D生成与编辑，提升多视角一致性和局部编辑能力。
摘要：
单图像3D生成在分层结构方面仍具有挑战性：学习的先验知识难以覆盖部分几何的长尾，并且难以保持多视角一致性，现有系统对精确局部编辑的支持有限。我们提出了PartRAG，一个结合外部部分数据库和扩散变换器的检索增强框架，将生成与可编辑的表示结合起来。为克服第一个挑战，我们引入了一个分层对比检索模块，将密集图像块与3D部分潜在向量在部分和物体粒度上对齐，从一个精心编纂的1,236个部分注释资产库中检索，将多样且物理上合理的示例注入去噪过程中。为克服第二个挑战，我们添加了一个掩码的部分编辑器，它在共享的规范空间中操作，允许交换、属性细化和组合更新，而无需重新生成整个物体，同时保持非目标部分和多视角一致性。PartRAG在Objaverse、ShapeNet和ABO上取得了竞争性的结果，将Chamfer距离从0.1726降低到0.1528，并将F-Score从0.7472提高到0.844在Objaverse上，推理时间为38秒，交互编辑时间为5-8秒。定性上，PartRAG产生了更清晰的部分边界，更好的细长结构保真度，并在活动物体上表现出鲁棒性。代码：https://github.com/AIGeeksGroup/PartRAG。网站：https://aigeeksgroup.github.io/PartRAG。
英文摘要：
Single-image 3D generation with part-level structure remains challenging: learned priors struggle to cover the long tail of part geometries and maintain multi-view consistency, and existing systems provide limited support for precise, localized edits. We present PartRAG, a retrieval-augmented framework that integrates an external part database with a diffusion transformer to couple generation with an editable representation. To overcome the first challenge, we introduce a Hierarchical Contrastive Retrieval module that aligns dense image patches with 3D part latents at both part and object granularity, retrieving from a curated bank of 1,236 part-annotated assets to inject diverse, physically plausible exemplars into denoising. To overcome the second challenge, we add a masked, part-level editor that operates in a shared canonical space, enabling swaps, attribute refinements, and compositional updates without regenerating the whole object while preserving non-target parts and multi-view consistency. PartRAG achieves competitive results on Objaverse, ShapeNet, and ABO-reducing Chamfer Distance from 0.1726 to 0.1528 and raising F-Score from 0.7472 to 0.844 on Objaverse-with inference of 38s and interactive edits in 5-8s. Qualitatively, PartRAG produces sharper part boundaries, better thin-structure fidelity, and robust behavior on articulated objects. Code: https://github.com/AIGeeksGroup/PartRAG. Website: https://aigeeksgroup.github.io/PartRAG.
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。
