CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation

📝 Original Info

• Title: CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation
• ArXiv ID: 2504.19174
• Date: 2025-04-27
• Authors: Xueqi Ma, Yilin Liu, Tianlong Gao, Qirui Huang, Hui Huang

📝 Abstract

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.

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