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.

翻译：直接从边界表示（B-rep）学习已显著推进了3D CAD分析。然而，最先进的B-rep学习方法依赖于绝对坐标和法向量来编码全局上下文，这使其对旋转高度敏感。我们的实验表明，在对齐基准测试中达到95%以上准确率的模型，在任意$\mathbf{SO}(3)$旋转下性能可能骤降至10%。为解决此问题，我们提出了FoV-Net，这是首个以旋转不变方式同时捕获局部表面几何与全局结构上下文的B-rep学习框架。每个面通过一个编码其局部表面几何的局部参考框架（LRF）UV网格，以及通过投射光线并记录与相邻面交点的视场（FoV）网格（用于捕获周围3D上下文）来表示。轻量级CNN提取每个面的特征，这些特征通过图注意力网络在B-rep图上传播。FoV-Net在B-rep分类和分割基准测试中实现了最先进的性能，展示了对任意旋转的鲁棒性，同时需要更少的训练数据即可取得优异结果。