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ICCV2025顶会

NeuFrameQ: Neural Frame Fields for Scalable and Generalizable Anisotropic Quadrangulation

Ying-Tian Liu, Jiajun Li, Yu-Tao Liu, Xin Yu, Yuan-Chen Guo, Yan-Pei Cao, Ding Liang, Ariel Shamir, Song-Hai Zhang

2025年份
6被引次数
2顶会引用

摘要

field prediction into direction regression and magnitude estimation tasks, we effectively handle the ill-posed nature in frame field estimation. We also employ the polyvector representation and attention mechanism in both tasks to handle the inherent ambiguities in frame field representation. Extensive experiments demonstrate that NeuFrameQ produces high-quality quad meshes with superior semantic alignment, also for geometries derived from neural fields. Our method significantly advances the state of the art in automatic quad mesh generation, bridging the gap between neural content creation and production-ready geometric assets.

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