Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects

TL;DR

Fit4CAD is a comprehensive benchmark dataset designed to evaluate and compare methods for fitting simple geometric primitives in point clouds of CAD objects, aiding developers and users in selecting effective tools.

Contribution

The paper introduces a new, expandable benchmark dataset with evaluation measures for primitive fitting in CAD point clouds, facilitating method comparison and development.

Findings

Demonstrated the benchmark's utility with two distinct primitive fitting methods.

Provided evaluation metrics for accuracy and performance.

Showcased the dataset's potential for future research and method improvement.

Abstract

We propose Fit4CAD, a benchmark for the evaluation and comparison of methods for fitting simple geometric primitives in point clouds representing CAD objects. 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 object. 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.