首个从点云生成纯四边形网格的深度学习方法，解决共面与凸性难题。
Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction
- 融合点与面特征，学习识别符合约束的四边形网格
- 在清点与噪声数据上均优于基线方法，生成高质量四边形网格
- 适合几何建模与计算力学领域，对网格生成研究有重要推动
四边形网格在几何建模和计算力学中至关重要。尽管基于学习的三角网格生成已取得显著进展，但四边形网格生成因需保证共面性、凸性和仅含四边形而研究较少。本文提出Point2Quad，首个基于学习的纯四边形网格生成方法。核心思路是通过融合点级与面级特征来学习四边形网格结构。首先基于k-NN生成候选，考虑共面性与正方形度；随后两个编码器分别提取几何与拓扑特征，以应对四边形相关约束，尤其结合深层四边形特异性特征；接着融合特征，使用设计的复合损失训练分类器；最终通过专用后处理优化结果。在清晰与噪声数据上的大量实验表明，Point2Quad在多项指标上均优于基线方法，验证了其有效性与优越性。
原文摘要 · Abstract (English)
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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