metadata
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 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:
@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}
}