# FEC: Fast Euclidean Clustering for Point Cloud Segmentation

arXiv: https://arxiv.org/abs/2208.07678 · HTML: https://mlsanity.com/paper/2208.07678/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 16 Aug 2022.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation](https://mlsanity.com/paper/1711.08588/) (2017)
- [3D Instance Segmentation via Multi-Task Metric Learning](https://mlsanity.com/paper/1906.08650/) (2019)
- [LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images](https://mlsanity.com/paper/1910.13955/) (2019)
- [FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-picking](https://mlsanity.com/paper/2012.14618/) (2020)

## Built on by (most cited first, 3 of 3)

- [Incremental Validation of Automated Driving Functions using Generic Volumes in Micro- Operational Design Domains](https://mlsanity.com/paper/2512.11351/) (2025)
- [Multi-Representation Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition](https://mlsanity.com/paper/2506.14243/) (2025)
- [RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation](https://mlsanity.com/paper/2508.19003/) (2025)

## Abstract (arXiv)

Segmentation from point cloud data is essential in many applications such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. In this paper, we present a fast solution to point cloud instance segmentation with small computational demands. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a pointwise scheme over the clusterwise scheme used in existing works. Our approach is conceptually simple, easy to implement (40 lines in C++), and achieves two orders of magnitudes faster against the classical segmentation methods while producing high-quality results.