用连续隐空间统一建模3D线框的几何与拓扑，生成更复杂多样的线框结构。
CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation
- 将曲线及其拓扑关系编码为连续隐向量，通过注意力VAE统一建模
- 流匹配模型从噪声生成隐向量，解码出完整3D线框，支持条件生成
- 适用于CAD设计、几何重建，生成结果在精度与多样性上领先
我们提出CLR-Wire，一种新型3D曲线线框生成框架，将几何与拓扑整合到统一的连续隐表示中。不同于传统方法将顶点、边、面分离处理，CLR-Wire利用注意力驱动的变分自编码器（VAE），将神经参数化曲线及其拓扑连接编码为固定长度的连续隐空间。该统一表示促进几何与拓扑的联合学习与生成。生成时采用流匹配模型，逐步将高斯噪声映射到这些隐向量，并解码为完整的3D线框。本方法可精细建模复杂形状与不规则拓扑，支持无条件生成及基于点云或图像的条件生成。实验表明，相比现有生成方法，本方法在准确性、新颖性和多样性上均有显著提升，为CAD设计、几何重建和3D内容创作提供高效全面的解决方案。
原文摘要 · Abstract (English)
We introduce CLR-Wire, a novel framework for 3D curve-based wireframe generation that integrates geometry and topology into a unified Continuous Latent Representation. Unlike conventional methods that decouple vertices, edges, and faces, CLR-Wire encodes curves as Neural Parametric Curves along with their topological connectivity into a continuous and fixed-length latent space using an attention-driven variational autoencoder (VAE). This unified approach facilitates joint learning and generation of both geometry and topology. To generate wireframes, we employ a flow matching model to progressively map Gaussian noise to these latents, which are subsequently decoded into complete 3D wireframes. Our method provides fine-grained modeling of complex shapes and irregular topologies, and supports both unconditional generation and generation conditioned on point cloud or image inputs. Experimental results demonstrate that, compared with state-of-the-art generative approaches, our method achieves substantial improvements in accuracy, novelty, and diversity, offering an efficient and comprehensive solution for CAD design, geometric reconstruction, and 3D content creation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。