# Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure

arXiv: https://arxiv.org/abs/2205.15426 · HTML: https://mlsanity.com/paper/2205.15426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 30 May 2022.

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

- [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)
- [Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds](https://mlsanity.com/paper/2106.03437/) (2021)
- [Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects](https://mlsanity.com/paper/2105.06858/) (2021)
- [Approximate implicitization using linear algebra](https://mlsanity.com/paper/1605.08638/) (2016)
- [Reverse engineering of CAD models via clustering and approximate implicitization](https://mlsanity.com/paper/1810.07451/) (2018)

## Built on by (most cited first, 2 of 2)

- [Recognising geometric primitives in 3D point clouds of mechanical CAD objects](https://mlsanity.com/paper/2301.04371/) (2023)
- [Few-shot Shape Recognition by Learning Deep Shape-aware Features](https://mlsanity.com/paper/2312.01315/) (2023)

## Abstract (arXiv)

The automatic creation of geometric models from point clouds has numerous applications in CAD (e.g., reverse engineering, manufacturing, assembling) and, more in general, in shape modelling and processing. Given a segmented point cloud representing a man-made object, we propose a method for recognizing simple geometric primitives and their interrelationships. Our approach is based on the Hough transform (HT) for its ability to deal with noise, missing parts and outliers. In our method we introduce a novel technique for processing segmented point clouds that, through a voting procedure, is able to provide an initial estimate of the geometric parameters characterizing each primitive type. By using these estimates, we localize the search of the optimal solution in a dimensionally-reduced parameter space thus making it efficient to extend the HT to more primitives than those that are generally found in the literature, i.e. planes and spheres. Then, we extract a number of geometric descriptors that uniquely characterize a segment, and, on the basis of these descriptors, we show how to aggregate parts of primitives (segments). Experiments on both synthetic and industrial scans reveal the robustness of the primitive fitting method and its effectiveness for inferring relations among segments.