SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images
[AAAI 2026]
Linfei Li · Lin Zhang* · Zhong Wang · Ying Shen
Raw Image info: ×ばつ10848, 189 MB -> 1.99 MB (.npz)
Table of Contents
conda create -n smartsplat python==3.12 conda activate smartsplat # install torch pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu124 pip install setuptools==78.0.1 pip install -r requirements.txt cd submodules/fused-ssim pip install -e . # Used by GaussianImage/3DGS/SmartSplat cd ../gsplat pip install -e . # Used by LIG cd ../gsplat2d pip install -e .
The datasets used in SmartSplat can be downloaded from the links provided below, including the tested subsets and the full versions hosted on Hugging Face or Baidu Netdisk.
| Dataset | Tested | Full |
|---|---|---|
| DIV8K | SmartSplat-DIV8K | Full DIV8K |
| DIV16K | SmartSplat-DIV16K | Full DIV16K |
This codebase integrates multiple GS-based image representation methods, including GaussianImage, 3DGS, and LIG. All our experiments were conducted on the A800 cluster. The corresponding run scripts are provided in the slurm folder.
LIG
data_path="data" base_log_path="logs" current_cr=50 python train_lig_for_eval.py \ -d $data_path \ --data_name DIV16K \ --model_name LIG \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000 \ --save_imgs
3DGS
data_path="data" base_log_path="logs" current_cr=50 python train_all_for_eval.py \ -d $data_path \ --data_name DIV8K \ --model_name 3DGS \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000
GaussianImage (RS)
data_path="data" base_log_path="logs" current_cr=50 python train_all_for_eval.py \ -d $data_path \ --data_name DIV16K \ --model_name GaussianImage_RS \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000
GaussianImage (Cholesky)
data_path="data" base_log_path="logs" current_cr=50 python train_all_for_eval.py \ -d $data_path \ --data_name DIV16K \ --model_name GaussianImage_Cholesky \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000
Image-GS
The
Image-GSimplementation used in our codebase is built uponGaussianImageand does not incorporate theTop-Kstrategy, resulting in suboptimal performance. For accurate reproduction, please refer to the official implementation.
data_path="data" base_log_path="logs" current_cr=50 python train_all_for_eval.py \ -d $data_path \ --data_name DIV8K \ --model_name ImageGS_RS \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000
SmartSplat
data_path="data" base_log_path="logs" current_cr=3000 python train_all_for_eval_smart.py \ -d $data_path \ --data_name DIV8K \ --model_name SmartSplat \ --compression_ratio $current_cr \ --log_dir $base_log_path \ --iterations 50000 \ --save_iter_img 10000
We thank the authors of the following repositories for their open-source code:
If you find our paper and code useful for your research, please use the following BibTeX entry.
@misc{li2025smartsplatfeaturesmartgaussiansscalable, title={SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images}, author={Linfei Li and Lin Zhang and Zhong Wang and Ying Shen}, year={2025}, eprint={2512.20377}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2512.20377}, }