发表机构
Zhejiang University; Hangzhou Dianzi University; Guangzhou University（浙江大学; 杭州电子科技大学; 广州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HiFi-BRep是一种新型框架，通过拓扑感知编码器与单阶段解码器解决B-Rep生成的脆弱性问题，在结构有效性和几何保真度上优于现有最优方法，提供了鲁棒的B-Rep合成方案。
AI 中文摘要
边界表示（B-Rep）生成是计算机辅助设计中的一项基础任务，但直接合成高保真且结构有效的B-Rep仍是重大挑战。现有深度生成方法存在两种脆弱性：表示脆弱性源于隐空间中的填充噪声和特征污染；生成脆弱性源于序列误差传播，以及因非可微有效性约束导致的训练-推理不匹配。我们提出HiFi-BRep，这一新颖框架通过两项协同贡献解决这些局限：一是拓扑感知编码器通过可学习查询消除填充，结合拓扑引导注意力防止特征污染，构建高保真隐式表示；二是单阶段解码器并行预测几何与拓扑，将核心流形约束嵌入为可微学习目标，该设计确保几何与拓扑间的相互引导，同时避免级联误差。大量实验表明，HiFi-BRep在结构有效性和几何保真度上均显著优于现有最优方法，为高质量B-Rep合成提供了鲁棒解决方案，代码和模型可在该https URL公开获取。
英文摘要
Boundary representation (B-Rep) generation is a fundamental task in computer-aided design, yet the direct synthesis of high-fidelity and structurally valid B-Reps remains a major challenge. Existing deep generative methods suffer from two forms of brittleness: representation brittleness, caused by padding noise and feature contamination in the latent space, and generation brittleness, stemming from sequential error propagation and a train-inference mismatch due to non-differentiable validity enforcement. We propose HiFi-BRep, a novel framework that addresses these limitations through two synergistic contributions. First, a topology-aware encoder constructs a high-fidelity latent representation by eliminating padding via learnable queries and preventing feature contamination with topology-guided attention. Second, a single-stage decoder jointly predicts geometry and topology in parallel, embedding core manifold constraints as a differentiable learning objective. This design ensures mutual guidance between geometry and topology while avoiding cascaded errors. Extensive experiments show that HiFi-BRep significantly outperforms state-of-the-art methods in both structural validity and geometric fidelity, providing a robust solution for high-quality B-Rep synthesis. Code and models are publicly available at https://github.com/1nnoh/HiFi-BRep.
CommentsAccepted to CVPR 2026
