| | --- |
| | 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} |
| | } |
