Point2Quad：基于面预测从点云生成四边形网格
Point2Quad: Generating Quad Meshes from Point Clouds via Face Prediction
AI总结：
提出Point2Quad，首个从点云生成纯四边形网格的学习方法，通过融合逐点和逐面特征并设计复合损失训练分类器，在清晰和噪声数据上均优于基线方法。
摘要：
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方法展示了显著进展，但由于难以确保共面性、凸性和纯四边形网格，四边形网格生成仍较少被探索。本文提出Point2Quad，这是首个从点云生成纯四边形网格的基于学习的方法。核心思想是通过融合逐点和逐面特征来识别四边形网格。具体而言，Point2Quad首先采用基于k-NN的候选生成方法，考虑共面性和正方形度。随后，使用两个编码器提取几何和拓扑特征，通过结合深入的四边形特定特性来解决四边形相关约束的挑战。接着，将提取的特征融合，利用设计的复合损失训练分类器。最终结果经过四边形特定后处理精炼得出。在清晰和噪声数据上的大量实验表明，Point2Quad在综合指标下相比基线方法具有有效性和优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
