HiFi-BRep：用于鲁棒B-Rep生成的高保真潜在表示

摘要

边界表示（B-Rep）生成是计算机辅助设计中的一项基本任务，然而直接合成高保真且结构有效的B-Rep仍然是一个重大挑战。现有的深度生成方法存在两种脆弱性：表示脆弱性，由潜在空间中的填充噪声和特征污染引起；以及生成脆弱性，源于顺序误差传播以及因不可微有效性约束而导致的训练-推理不一致。我们提出了HiFi-BRep，一种通过两项协同贡献解决这些局限性的新框架。首先，拓扑感知编码器通过可学习查询消除填充，并利用拓扑引导注意力防止特征污染，从而构建高保真的潜在表示。其次，单阶段解码器并行联合预测几何与拓扑，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之间的相互引导，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方面均显著优于现有最先进方法，为高质量B-Rep合成提供了一种稳健的解决方案。代码和模型已公开于 https://github.com/1nnoh/HiFi-BRep。

English

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.