---
license: mit
tags:
- artifact
- HPCA
- point-cloud
- 3d-vision
- fractal
- docker
- pretrained-models
paper:
  title: "FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing"
  venue: "HPCA 2026"
  url: "https://github.com/Yuzhe-Fu/FractalCloud"
---

# FractalCloud Artifact Repository

This repository provides the **Docker image** and **pretrained models** for the HPCA’26 paper:

> **FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing**

The **official implementation** (including full source code, training pipelines, and evaluation scripts) is available at:  
👉 https://github.com/Yuzhe-Fu/FractalCloud


## Contents

- **Docker image** for reproducing all experiments in the paper  
- **Pretrained models** for classification and segmentation tasks  
- Fully packaged environment with all dependencies included  


## Usage

Please refer to the [official repository](https://github.com/Yuzhe-Fu/FractalCloud) for instructions on:
- environment setup
- dataset preparation
- inference, training, and finetuning
- experiment reproduction

All steps follow the procedure described in the paper and the official codebase.


## Citation

If you find this repository useful in your research, please cite:

```bibtex
@inproceedings{fu2026fractalcloud,
  title     = {FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing},
  author    = {Fu, Yuzhe and Zhou, Changchun and Ye, Hancheng and Duan, Bowen and Huang, Qiyu and Wei, Chiyue and Guo, Cong and Li, Hai and Chen, Yiran},
  booktitle = {Proceedings of the 2026 IEEE International Symposium on High-Performance Computer Architecture (HPCA)},
  year      = {2026}
}