# HiFi-BRep：面向鲁棒 B-Rep 生成的高保真潜在表示

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-17 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmsy9dola0p2hroz0y2c9s3lb
- 原文链接：https://arxiv.org/abs/2608.16485

## AI 摘要

HiFi-BRep 提出一种新型框架，通过拓扑感知编码器消除填充噪声与特征污染，并以单阶段解码器并行预测几何与拓扑，将流形约束嵌入可微学习目标。实验表明，该方法在结构有效性与几何保真度上显著优于现有最先进方法，代码与模型已公开。

## 正文

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.