Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair

• URL: http://arxiv.org/abs/2608.04955v2
• Date: Thu, 06 Aug 2026 04:21:17 GMT
• Title: Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair
• Abstract summary: Multistage representation (triggered computation generation) generates CAD models.<n>Risks such as self-intersections propagate invalid final validity of BReps.<n>We propose a framework to intervene at the Wireframe Detection stage to improve downstream BRep validity.
• Score: 11.44214616902057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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