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arXiv · 2504.19545

Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction

Zezeng Li·Zhihui Qi·Weimin Wang·Ziliang Wang·Junyi Duan·Na Lei
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Abstract

Quad meshes are essential in geometric modeling and computational mechanics. Although learning-based methods for triangle mesh demonstrate considerable advancements, quad mesh generation remains less explored due to the challenge of ensuring coplanarity, convexity, and quad-only meshes. In this paper, we present Point2Quad, the first learning-based method for quad-only mesh generation from point clouds. The key idea is learning to identify quad mesh with fused pointwise and facewise features. Specifically, Point2Quad begins with a k-NN-based candidate generation considering the coplanarity and squareness. Then, two encoders are followed to extract geometric and topological features that address the challenge of quad-related constraints, especially by combining in-depth quadrilaterals-specific characteristics. Subsequently, the extracted features are fused to train the classifier with a designed compound loss. The final results are derived after the refinement by a quad-specific post-processing. Extensive experiments on both clear and noise data demonstrate the effectiveness and superiority of Point2Quad, compared to baseline methods under comprehensive metrics.

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Zezeng Li, Zhihui Qi, Weimin Wang, Ziliang Wang, Junyi Duan, Na Lei. 2025-04-28. Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction. https://doi.org/10.1109/tcsvt.2025.3556130

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