用流匹配构建连续紧凑的3D形状隐空间表示，支持零样本法向与形变估计。
3D Shape Tokenization via Latent Flow Matching
- 在3D空间中用流匹配建模表面为概率密度函数，仅需点云输入
- 实现零样本表面法向与形变场估计，生成任务性能媲美基线
- 适合需要低预处理、高几何保真的3D生成与理解任务
我们提出一种基于流匹配的3D隐空间表示，将3D表面建模为三维空间中的概率密度函数p(x,y,z)。该表示专为机器学习模型设计，天然具备连续性与紧凑性，仅需点云输入且预处理极少。尽管是数据驱动方法，其在3D空间中使用流匹配，展现出有趣几何特性，包括零样本估计表面法向与形变场的能力。我们在多个机器学习任务上进行评估，包括3D-CLIP、无条件生成模型、单图像条件生成模型及交点估计。所有实验均达到与现有基线相当的性能，同时显著减少数据预处理和训练所需的辅助信息。
原文摘要 · Abstract (English)
We introduce a latent 3D representation that models 3D surfaces as probability density functions in 3D, i.e., p(x,y,z), with flow-matching. Our representation is specifically designed for consumption by machine learning models, offering continuity and compactness by construction while requiring only point clouds and minimal data preprocessing. Despite being a data-driven method, our use of flow matching in the 3D space enables interesting geometry properties, including the capabilities to perform zero-shot estimation of surface normal and deformation field. We evaluate with several machine learning tasks, including 3D-CLIP, unconditional generative models, single-image conditioned generative model, and intersection-point estimation. Across all experiments, our models achieve competitive performance to existing baselines, while requiring less preprocessing and auxiliary information from training data.
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