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

arXiv: https://arxiv.org/abs/2105.06858 · HTML: https://mlsanity.com/paper/2105.06858/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 7 of 7)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [Thingi10K: A Dataset of 10,000 3D-Printing Models](https://mlsanity.com/paper/1605.04797/) (2016)
- [Learning Shape Abstractions by Assembling Volumetric Primitives](https://mlsanity.com/paper/1612.00404/) (2016)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [Parsing Geometry Using Structure-Aware Shape Templates](https://mlsanity.com/paper/1808.01337/) (2018)
- [Approximate implicitization using linear algebra](https://mlsanity.com/paper/1605.08638/) (2016)

## Built on by (most cited first, 4 of 4)

- [Recognising geometric primitives in 3D point clouds of mechanical CAD objects](https://mlsanity.com/paper/2301.04371/) (2023)
- [A Survey of Methods for Converting Unstructured Data to CSG Models](https://mlsanity.com/paper/2305.01220/) (2023)
- [Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure](https://mlsanity.com/paper/2205.15426/) (2022)
- [Imitation of Manipulation Skills Using Multiple Geometries](https://mlsanity.com/paper/2203.01171/) (2022)

## Abstract (arXiv)

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.