This paper presents the results of SHREC’21 track: Quantifying Shape Complexity. Our goal is to investigate how good the submitted shape complexity measures are (i.e. with respect to ground truth) and investigate the relationships between these complexity measures (i.e. with respect to correlations). The dataset consists of three collections: 1800 perturbed cube and sphere models classified into 4 categories, 50 shapes inspired from the fields of architecture and design classified into 2 categories, and the data from the Princeton Segmentation Benchmark, which consists of 19 natural object categories. We evaluate the performances of the methods by computing Kendall rank correlation coefficients both between the orders produced by each complexity measure and the ground truth and between the pair of orders produced by each pair of complexity measures. Our work, being a quantitative and reproducible analysis with justified ground truths, presents an improved means and methodology for the evaluation of shape complexity.

本文介绍了 SHREC'21 track: Quantifying Shape Complexity 的结果。我们的目标是调查提交的形状复杂性度量的好坏（即关于真实情况）并调查这些复杂性度量之间的关系（即关于相关性）。该数据集由三个集合组成：1800 个扰动立方体和球体模型，分为 4 个类别，50 个受建筑和设计领域启发的形状分为 2 个类别，以及来自普林斯顿分割基准的数据，其中包含 19 个自然对象类别。我们通过计算每个复杂性度量产生的阶数与基本事实之间以及每对复杂性度量产生的阶数对之间的 Kendall 等级相关系数来评估这些方法的性能。我们的工作是具有合理基础事实的定量和可重复分析，为评估形状复杂性提供了一种改进的手段和方法。