SIGGRAPH 2025 | CLR-Wire：曲线框可生成？可交互？深大VCC带你见证魔法

Core Insights - The article discusses the innovative CLR-Wire technology developed by a team from Shenzhen University, which enables the encoding of complex 3D wireframe structures into a continuous latent space, addressing challenges in capturing both geometric and topological information effectively [1][5]. Group 1: Technology Overview - CLR-Wire allows for efficient generation and smooth interpolation of complex 3D structures, with applications in industrial design, 3D reconstruction, and content creation [1]. - The technology integrates geometric curves and topological structures into a continuous latent space, facilitating smooth transitions between different 3D wireframes [5][14]. - The method employs a multi-layer cross-attention mechanism to encode neural parametric curves and their discrete topological relationships into fixed-length latent vectors, utilizing a variational autoencoder to construct a continuous latent space distribution [8][14]. Group 2: Key Modules - The CurveVAE module standardizes 3D geometric curves to enhance training stability and utilizes cross-attention for dimensionality reduction, ultimately achieving continuous reconstruction of curves [13][14]. - The WireframeVAE module combines latent vectors, vertex coordinates, and adjacency relationships into a global latent vector, ensuring efficient fusion of geometric and topological information for high-quality reconstruction [15][17]. - The Flow Matching module generates wireframe samples from noise by training a velocity field network, allowing for both unconditional and conditional generation based on point clouds or images [17][18]. Group 3: Performance Evaluation - CLR-Wire outperforms existing methods in generating wireframes, demonstrating superior coverage, lower distribution differences, and high fidelity in geometric details [19][21]. - In unconditional generation tasks, CLR-Wire shows significant advantages over methods like 3DWire, DeepCAD, and BrepGen, particularly in generating diverse and detailed freeform wireframes [19][21]. - The method also excels in conditional generation scenarios, effectively reconstructing wireframes from sparse point clouds and single-view images, showcasing its robustness against incomplete data [24][26]. Group 4: Future Directions - While CLR-Wire has demonstrated smooth interpolation capabilities, further research is needed to enhance controllable generation and editing features [28]. - Future developments may focus on aligning the latent space more closely with textual descriptions to achieve higher levels of semantic-driven control [28].