新的SO(3)-等变图神经网络处理CAD边界表示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了SO3brep，一种新颖的SO(3)-等变图神经网络，用于处理CAD边界表示（B-reps）。该新模型通过在消息传递过程中保留面之间的相对方向信息，解决了从非规范方向存储的CAD模型中学习的挑战。与现有的B-rep和等变点云网络相比，SO3brep在多个基准测试中表现出更高的准确性和参数效率，尤其是在标记数据有限的情况下，这在工业CAD应用中很常见。 AI

影响引入了一种更鲁棒的从3D CAD数据中学习的方法，有望改进设计、制造和机器人领域的应用。

排序理由详细介绍新型图神经网络架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Matteo Ballegeer, Dries F. Benoit · 2026-11-05 05:00

SO(3)-Equivariant Learning on CAD Boundary Representations

arXiv:2611.05449v1 Announce Type: cross Abstract: Learning on boundary representations (B-reps) requires robustness to the pose of a part, since CAD models are not stored in a canonical orientation. Existing B-rep networks either operate on world-frame coordinates and normals, an…

本文如何被排名

报道来源 [1]

SO(3)-Equivariant Learning on CAD Boundary Representations

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