AIHOT 于 2026-08-17 收录了“HiFi-BRep：面向鲁棒 B-Rep 生成的高保真潜在表示”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
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已抓取公开正文公开原文内容
[2608.16485] HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation
Computer Science > Computer Vision and Pattern Recognition
arXiv:2608.16485 (cs)
-
[Submitted on 17 Aug 2026 (v1), last revised 18 Aug 2026 (this version, v2)]
Title:HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation
Authors:Junhao Hou, Chenqi Luo, Pufan Wang, Jiaying Lu, Yusheng Liu, Feiwei Qin, Meie Fang, Kun Zhou
View a PDF of the paper titled HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation, by Junhao Hou and 7 other authors
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Abstract: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 this https URL.
Comments:
Accepted to CVPR 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16485 [cs.CV]
(or
arXiv:2608.16485v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16485
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Junhao Hou [view email]
[v1]
Mon, 17 Aug 2026 12:25:26 UTC (19,050 KB)
[v2]
Tue, 18 Aug 2026 02:23:14 UTC (17,156 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation, by Junhao Hou and 7 other authors
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arXiv:2608.16485 (cs)
-
[Submitted on 17 Aug 2026 (v1), last revised 18 Aug 2026 (this version, v2)]
Title:HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation
Authors:Junhao Hou, Chenqi Luo, Pufan Wang, Jiaying Lu, Yusheng Liu, Feiwei Qin, Meie Fang, Kun Zhou
View a PDF of the paper titled HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation, by Junhao Hou and 7 other authors
View PDF
HTML (experimental)
Abstract: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 this https URL.
Comments:
Accepted to CVPR 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2608.16485 [cs.CV]
(or
arXiv:2608.16485v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.16485
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Junhao Hou [view email]
[v1]
Mon, 17 Aug 2026 12:25:26 UTC (19,050 KB)
[v2]
Tue, 18 Aug 2026 02:23:14 UTC (17,156 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation, by Junhao Hou and 7 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.CV
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new
|
recent
| 2026-08
Change to browse by:
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References & Citations
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export BibTeX citation
Loading...
BibTeX formatted citation
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Data provided by:
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AIHOT 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。
为什么值得关注
该工作把流形约束直接作为可微学习目标，配合几何与拓扑的并行预测，缓解了 B-Rep 生成中常见的级联误差，对 CAD 自动化建模方法有实际参考价值。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：该工作把流形约束直接作为可微学习目标，配合几何与拓扑的并行预测，缓解了 B-Rep 生成中常见的级联误差，对 CAD 自动化建模方法有实际参考价值。
- AIHOT 评分：53；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。