AI 中文总结
该研究针对CAD模型B-Rep生成中线框的几何拓扑异常问题，提出无需训练的WDR框架，通过GTAD检测风险、EGGTR修复，提升了B-Rep有效性且保留模型质量，可集成至多种生成流水线。
AI 中文摘要
多阶段边界表示（B-Rep）生成利用中间线框来合成CAD模型，但这些线框中存在几何和拓扑风险，如自相交、边坍塌和不连通顶点，会传播导致最终B-Rep无效。通过重新训练大型生成模型来缓解此类故障的计算成本过高。我们提出Wireframe Detection and Repair（WDR，线框检测与修复），这是一个无需训练的框架，可在中间线框阶段进行干预以提升下游B-Rep的有效性。WDR包含几何-拓扑异常检测器（GTAD），它结合基于并行视觉语言模型（VLM）的粗筛选与几何和拓扑检测器，预测下游无效风险并将生成任务路由至专用分支；随后能量引导的几何-拓扑修复（EGGTR）模块通过几何和拓扑分支执行检测器触发的引导式再生。WDR通过能量引导重采样扩展测试时计算，并对扩散模型采用无需训练的引导，可集成到自回归和扩散流水线中且无需重新训练。大量实验表明，WDR在经内核检查的有效性上实现了持续提升，同时基本保留了合成CAD模型的测量多样性和分布质量。代码将在论文接收后公开。
英文摘要
Multi-stage boundary representation (B-Rep) generation leverages intermediate wireframes to synthesize CAD models. However, geometric and topological risks in these wireframes -- such as self-intersections, edge collapses, and disconnected vertices -- can propagate to invalid final B-Reps. Mitigating such failures by retraining large generative models is computationally prohibitive. We propose Wireframe Detection and Repair (WDR), a training-free framework that intervenes at the intermediate wireframe stage to improve downstream B-Rep validity. WDR features a Geometric-Topology Anomaly Detector (GTAD) that combines parallel VLM-based coarse screening with geometric and topological detectors to predict downstream invalidity risk and route generation to dedicated branches. An Energy-Guided Geometric-Topology Repair (EGGTR) module then performs detector-triggered guided regeneration through geometry and topology branches. By scaling test-time computation via Energy-Guided Resampling and training-free guidance for diffusion models, WDR can be integrated into autoregressive and diffusion pipelines without retraining. Extensive experiments demonstrate consistent improvements in kernel-checked validity while largely retaining the measured diversity and distributional quality of synthesized CAD models. The code will be made publicly available upon acceptance.
