# Recognising geometric primitives in 3D point clouds of mechanical CAD objects

arXiv: https://arxiv.org/abs/2301.04371 · HTML: https://mlsanity.com/paper/2301.04371/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds](https://mlsanity.com/paper/2003.12181/) (2020)
- [Supervised Fitting of Geometric Primitives to 3D Point Clouds](https://mlsanity.com/paper/1811.08988/) (2018)
- [Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders](https://mlsanity.com/paper/2112.09329/) (2021)
- [HPNet: Deep Primitive Segmentation Using Hybrid Representations](https://mlsanity.com/paper/2105.10620/) (2021)
- [Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits](https://mlsanity.com/paper/1901.01255/) (2019)
- [PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation](https://mlsanity.com/paper/2112.13942/) (2021)
- [Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects](https://mlsanity.com/paper/2105.06858/) (2021)
- [Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure](https://mlsanity.com/paper/2205.15426/) (2022)
- [SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds](https://mlsanity.com/paper/2206.07636/) (2022)

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

- [Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds](https://mlsanity.com/paper/2312.04962/) (2023)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Few-shot Shape Recognition by Learning Deep Shape-aware Features](https://mlsanity.com/paper/2312.01315/) (2023)
- [Deep Unsupervised Segmentation of Log Point Clouds](https://mlsanity.com/paper/2503.14244/) (2025)

## Abstract (arXiv)

The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds resulting from scans of mechanical CAD objects. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. In this article we propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts. Additionally, we can extract a series of geometric descriptors that uniquely characterize a primitive and, based on them, aggregate the output into maximal or compound primitives, thus reducing oversegmentation. The results presented in the paper demonstrate the robustness of the method and its competitiveness with respect to other solutions proposed in the literature.