# FEC

**Repository Path**: YaoFL/FEC

## Basic Information

- **Project Name**: FEC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-23
- **Last Updated**: 2024-12-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# FEC: Fast Euclidean Clustering for Point Cloud Segmentation

[![Open In Colab][image-1]][1]  [![Binder][image-2]][2]

<p>
  <a href="https://github.com/sindresorhus/123"><img src="https://camo.githubusercontent.com/abb97269de2982c379cbc128bba93ba724d8822bfbe082737772bd4feb59cb54/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667"></a>
  <a href="https://creativecommons.org/licenses/by/4.0/"><img src="https://camo.githubusercontent.com/bca967b18143b8a5b2ffe78bd4a1a30f6bc21de83bd8336f748e96498af38b38/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d43432532304259253230342e302d6c69676874677265792e737667"></a>
  <a href="https://creativecommons.org/licenses/by/4.0/"><img src="https://camo.githubusercontent.com/33126b4770aa6f169b2a93e75678d52647f19972fa8d205e478049966e3b1a07/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63732d737461626c652d627269676874677265656e2e7376673f7374796c653d666c6174266c6f6e6743616368653d74727565"></a>
  <a href="https://github.com/allegroai/clearml"><img src="https://camo.githubusercontent.com/f60861e75a851f69a1fb8a5c671ef233147b7781a13dae226dcc2c32166654c0/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f636c6561726d6c2e737667"></a>
</p>

<p align="center"><a href="./README_CN.md">English</a> / [简体中文]</p>

这是**FEC**的 _唯一官方_ 代码仓。

代码仓永久保留在[here][3].

**每天都在完善中，敬请期待！**

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. A fast solution for point cloud instance segmentation with small computational demands is lacking. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a point-wise scheme over the cluster-wise scheme used in existing works. The proposed method avoids traversing every point constantly in each nested loop, which is time and memory-consuming. 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.

## 维护

[@ YizhenLAO ][4]
[@IfeiHsueh][5]

## License

[MIT]()(LICENSE) © Yizhen LAO

[1]:  https://colab.research.google.com/github/bipinKrishnan/fastai_course/blob/master/bear_classifier.ipynb
[2]:  https://mybinder.org/v2/gh/bipinKrishnan/fastai_course/master
[3]:  https://github.com/YizhenLAO/FEC
[4]:  https://github.com/YizhenLAO
[5]:  https://github.com/IfeiHsueh


[image-1]:  https://colab.research.google.com/assets/colab-badge.svg
[image-2]:  https://mybinder.org/badge_logo.svg