论文标题
SHREC 2022：对点云上简单几何原语的拟合和识别
SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds
论文作者
论文摘要
本文介绍了参与SHREC 2022曲目的方法，以拟合点云上简单几何原始图的拟合和识别。作为简单的原始素，我们的意思是从建设性固体几何形状（即平面，球体，圆柱体，锥体和圆锥形）得出的经典表面原语。曲目的目的是评估适合和识别点云上的几何原始算法的自动算法质量。具体而言，目标是确定每个点云，其原始类型和一些几何描述符。为此，我们创建了一个合成数据集，该数据集分为一个训练集和一个测试集，其中包含与不同类型的点云伪像扰动的片段。在该曲目的六名参与者中，两个是基于直接方法，而四个是完全基于深度学习的，或者是直接和神经方法。使用各种分类和近似度量评估方法的性能。
This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cones and tori. The aim of the track is to evaluate the quality of automatic algorithms for fitting and recognising geometric primitives on point clouds. Specifically, the goal is to identify, for each point cloud, its primitive type and some geometric descriptors. For this purpose, we created a synthetic dataset, divided into a training set and a test set, containing segments perturbed with different kinds of point cloud artifacts. Among the six participants to this track, two are based on direct methods, while four are either fully based on deep learning or combine direct and neural approaches. The performance of the methods is evaluated using various classification and approximation measures.
