提出首个旋转不变的CAD模型学习框架，解决传统方法对旋转敏感问题。
FoV-Net: Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Casting
- 用局部参考系和视域射线投射编码几何与结构上下文
- 在任意旋转下仍保持95%以上准确率，远超现有方法
- 适合需要鲁棒3D建模的工业设计与逆向工程场景
直接从边界表示（B-reps）学习已显著推动3D CAD分析。然而，当前先进B-rep学习方法依赖绝对坐标与法向量编码全局上下文，对旋转高度敏感。实验表明，某些模型在对齐基准上准确率达95%以上，但在任意SO(3)旋转下性能可降至仅10%。为此，我们提出FoV-Net，首个实现旋转不变性B-rep学习框架。每个面通过局部参考系（LRF）UV网格编码局部几何，并通过视域（FoV）网格捕捉周围3D上下文，方法是投射射线并记录与邻接面的交点。轻量级CNN提取每面特征，再经图注意力网络在B-rep图上传播。FoV-Net在B-rep分类与分割基准上达到最先进性能，同时在任意旋转下保持鲁棒性，且训练所需数据更少。
原文摘要 · Abstract (English)
Learning directly from boundary representations (B-reps) has significantly advanced 3D CAD analysis. However, state-of-the-art B-rep learning methods rely on absolute coordinates and normals to encode global context, making them highly sensitive to rotations. Our experiments reveal that models achieving over 95% accuracy on aligned benchmarks can collapse to as low as 10% under arbitrary $\mathbf{SO}(3)$ rotations. To address this, we introduce FoV-Net, the first B-rep learning framework that captures both local surface geometry and global structural context in a rotation-invariant manner. Each face is represented by a Local Reference Frame (LRF) UV-grid that encodes its local surface geometry, and by Field-of-View (FoV) grids that capture the surrounding 3D context by casting rays and recording intersections with neighboring faces. Lightweight CNNs extract per-face features, which are propagated over the B-rep graph using a graph attention network. FoV-Net achieves state-of-the-art performance on B-rep classification and segmentation benchmarks, demonstrating robustness to arbitrary rotations while also requiring less training data to achieve strong results.
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