We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD models. This benchmark is meant to help both method developers and those who want to identify the best performing tools. The Fit4CAD dataset is composed by 225 high quality point clouds, each of which has been obtained by sampling a CAD model. The way these elements were created by using existing platforms and datasets makes the benchmark easily expandable. The dataset is already split into a training set and a test set. To assess performance and accuracy of the different primitive fitting methods, various measures are defined. To demonstrate the effective use of Fit4CAD, we have tested it on two methods belonging to two different categories of approaches to the primitive fitting problem: a clustering method based on a primitive growing framework and a parametric method based on the Hough transform.
翻译:我们提出了Fit4CAD, 用于评估和比较在代表 CAD 模型的点云中安装简单几何原始物的方法的基准。 该基准旨在帮助方法开发者和那些想要确定最佳工具的人。 Fit4CAD 数据集由225个高质量的高点云组成,每个高点云都是通过取样一个 CAD 模型获得的。 这些元素是如何通过利用现有平台和数据集创建的,使得基准易于扩展的。 数据集已经分为一组培训和一组测试。 为了评估不同原始安装方法的性能和准确性,我们确定了各种措施。 为了证明Fit4CAD 的有效利用,我们测试了两种方法,两种方法属于两种不同类别的原始安装问题:一种基于原始生长框架的集群法和一种基于Hough变换的参数法。