SAM3D-Part：从3D物体进行交互式部件选择与生成
SAM3D-Part: Interactive Part Selection and Generation from 3D Objects
- SSE, CUHKSZ（香港中文大学（深圳）理工学院）
- FNii-Shenzhen（深圳市福田国家创新中心）
- Meshy AI
- Nanjing University of Science and Technology（南京理工大学）
- MBZUAI（穆罕默德·本·扎耶德人工智能大学）
- GenuX
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SAM3D-Part提出一个提示驱动框架，从3D网格中按需选择并生成完整部件，通过特征融合与逐体素对应实现高精度对齐，支持多部件一致生成。
AI中文摘要：
部件级控制对于现代3D资产生成至关重要，其中物体经常通过其各个组成部分进行编辑、重用、动画化或制造。在许多此类工作流程中，用户仅需要几个特定部件，而非完整的物体分解。然而，现有的3D生成方法无论用户意图如何都会生成所有部件，而可提示的3D分割方法通常输出部分表面而非可重用的完整网格。此外，图像条件部件生成器在未直接以源网格为条件时，难以保持隐藏几何形状和准确放置。为解决这些问题，我们提出了SAM3D-Part，一个用于从输入3D物体网格进行选择性部件生成的提示驱动框架。给定源网格和部件提示，SAM3D-Part首先将源几何编码为紧凑的网格特征，并通过像素级通道融合将其与渲染图像、选择性掩码和点图观测对齐。融合后的表示条件化一个前馈生成模型，仅生成所查询的部件作为完整网格。为了将生成的部件放回源坐标系，SAM3D-Part预测密集的逐体素对应关系，并从分布的空间证据而非单一全局姿态码估计部件变换。对于顺序的多部件查询，先前生成的部件存储在部件缓存中并作为上下文约束重用，减少独立请求部件之间的冲突。大量实验和消融研究表明，SAM3D-Part能显著改善源对齐，降低条件化成本，并实现一致的选择性部件生成，达到最先进水平。代码和权重将在该https URL提供。
英文摘要：
Part-level control is essential for modern 3D asset creation, where objects are frequently edited, reused, animated, or fabricated through their individual components. In many such workflows, users need only several specific components rather than a complete object decomposition. However, existing 3D generation methods produce all parts regardless of user intent, while promptable 3D segmentation methods typically output partial surfaces instead of reusable complete meshes. In addition, image-conditioned part generators further struggle to preserve hidden geometry and accurate placement without directly conditioning on the source mesh. To address these problems, we present SAM3D-Part, a prompt-driven framework for selective part generation from input 3D object meshes. Given a source mesh and a part prompt, SAM3D-Part first encodes the source geometry into compact mesh features and aligns them with the rendered image, selective mask, and point-map observations via pixel-wise channel fusion. The fused representation conditions a feed-forward generative model to produce only the queried component as a completed mesh. To place the generated part back into the source coordinate frame, SAM3D-Part predicts dense per-voxel correspondences and estimates the part transformation from distributed spatial evidence rather than a single global pose code. For sequential multi-part queries, previously generated parts are stored in a part cache and reused as contextual constraints, reducing conflicts among independently requested components. Extensive experiments and ablations demonstrate that SAM3D-Part can significantly improve source alignment, reduce conditioning cost, and enable consistent selective part generation, achieving state-of-the-art. Code and weights will be available at https://github.com/Jiahao620/sam3d-part.
