论文标题
预测3D点云中差异特性的几何关注
Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
论文作者
论文摘要
离散3D数据表示中差异几何量的估计是几何处理管道中的关键步骤之一。具体而言，估计原始点云的概念范围和尖锐的特征线有助于提高网格序列的质量，并使我们能够使用更精确的表面重建技术。在设计解决此类问题的可学习方法时，主要困难是在点云中选择社区并在点之间结合几何关系。在这项研究中，我们提出了一种几何注意机制，可以以可学习的方式提供此类属性。我们通过对正常向量的预测和特征线提取的几个实验来确定所提出的技术的有用性。
Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline. Specifically, estimating normals and sharp feature lines from raw point cloud helps improve meshing quality and allows us to use more precise surface reconstruction techniques. When designing a learnable approach to such problems, the main difficulty is selecting neighborhoods in a point cloud and incorporating geometric relations between the points. In this study, we present a geometric attention mechanism that can provide such properties in a learnable fashion. We establish the usefulness of the proposed technique with several experiments on the prediction of normal vectors and the extraction of feature lines.
