🤖 AI Summary
This work addresses the limitations of existing approaches to machining feature recognition in B-Rep CAD models—namely, low sample efficiency, high computational cost, and the decoupled evaluation of instance segmentation and semantic labeling—by introducing FeatureFox, a lightweight and efficient panoptic recognition pipeline. FeatureFox employs an enhanced binary edge classifier with enriched edge attributes to localize feature boundaries, extracts connected components from a pruned face adjacency graph to recover instances, and predicts machining categories via subgraph aggregation. Notably, it is the first to adopt the Panoptic Quality (PQ) metric for this task, enabling end-to-end joint optimization. With only around 250 training parts and seconds of training time, FeatureFox achieves PQ > 0.9, matching the performance of the deep learning model AAGNet on the full dataset while demonstrating superior feature localization accuracy and generalization capability.
📝 Abstract
Automatic feature recognition (AFR) on B-Rep 3D-CAD models is central to CAD/CAM automation, yet most learning-based methods are complex, data-hungry, and evaluate instance grouping and semantic labeling separately. We present FeatureFox, a panoptic AFR pipeline that outputs machining instances with semantic labels: a calibrated binary edge classifier on enriched edge attributes localizes feature boundaries, instances are recovered as connected components in a pruned face-adjacency graph, and a per-instance classifier predicts the machining class from aggregated subgraph attributes. We evaluate on MFInstSeg using Panoptic Quality (PQ), which jointly scores instance separation and semantic correctness. FeatureFox is substantially more sample- and compute-efficient than the deep baseline AAGNet, reaching $\mathrm{PQ}>0.9$ with $\sim250$ training parts versus $\sim5{,}000$ for AAGNet, and training on the full MFInstSeg set takes seconds on a GPU. On the full training set, AAGNet surpasses FeatureFox marginally in PQ, while FeatureFox remains slightly ahead in feature-level recognition and localization accuracy. Finally, leveraging its low data requirement, we train FeatureFox on $270$ manually labeled industrial CAD parts and show qualitative generalization to an unseen real industrial part, indicating practical real-world applicability.