Initial dataset card for Voxel-GS

README.md

@@ -0,0 +1,92 @@
+---
+task_categories:
+- OTHER
+tags:
+- gaussian-splatting
+- 3d-compression
+---
+
+# Voxel-GS Dataset
+
+This repository contains datasets and resources related to the paper [Voxel-GS: Quantized Scaffold Gaussian Splatting Compression with Run-Length Coding](https://huggingface.co/papers/2512.17528).
+
+Voxel-GS proposes a simple yet highly effective framework for compressing substantial Gaussian splatting format point clouds. It achieves competitive performance using a lightweight rate proxy and run-length coding, departing from complex neural entropy models.
+
+**Paper:** [https://huggingface.co/papers/2512.17528](https://huggingface.co/papers/2512.17528)
+**Code:** [https://github.com/zb12138/VoxelGS](https://github.com/zb12138/VoxelGS)
+
+### Dataset Structure
+The `voxelGS/data` directory, as described in the associated GitHub repository, contains various 3D scene datasets structured as follows:
+
+```
+voxelGS/data
+├── DeepBlending 
+    ├── drjohnson
+    └── playroom
+├── MipNerf360 
+    ├── bicycle
+    ├── bonsai
+    ├── counter
+    ├── flowers
+    ├── garden
+    ├── kitchen
+    ├── room
+    ├── stump
+    └── treehill
+├── Nerf_Synthetic 
+    ├── chair
+    ├── drums
+    ├── ficus
+    ├── hotdog
+    ├── lego
+    ├── materials
+    ├── mic
+    └── ship
+└── T2T 
+    ├── train
+    └── truck
+```
+
+### Sample Usage
+
+The following sections provide instructions for setting up the environment and using the `Train.py` and `Coder.py` scripts from the [GitHub repository](https://github.com/zb12138/VoxelGS) to interact with Voxel-GS.
+
+#### Environment Setup
+To set up the environment with cuda 11.7 and python 3.8:
+
+```bash
+conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
+pip install omegaconf,loguru,open3d==0.19.0,opencv-python,plyfile,tensorboard,termcolor,torch_scatter,jaxtyping,einops,lpips
+pip install models/submodules/* # from Scaffold-GS
+```
+
+#### Training
+You can train the model using `Train.py`. After training, `stat_log` will generate statistical information and save the results in the output folder.
+
+```bash
+python Train.py
+```
+
+Alternatively, you can [download pre-compressed binary files](https://huggingface.co/datasets/zb1213899/VoxelGS_BIN/tree/main) and decompress them directly:
+
+```bash
+python Coder.py -bin Results/BIN/DeepBlending/playroom/gsbin -eval
+```
+
+#### Encoding, Decoding, and Evaluation
+The `Coder.py` script can be used for encoding PLY files, decoding binary files, and for visualization and evaluation.
+
+1.  **Encode** a PLY file:
+    ```bash
+    python Coder.py -ply output/base/Nerf_Synthetic/hotdog/point_cloud/point_cloud_30000.quantized.ply -encode
+    ```
+
+2.  **Decode** a binary (gsbin) file:
+    ```bash
+    python Coder.py -bin output/base/Nerf_Synthetic/hotdog/gsbin
+    ```
+
+3.  **Visualize and Evaluate** a binary (gsbin) file:
+    ```bash
+    python Coder.py -bin output/base/Nerf_Synthetic/hotdog/gsbin -show -eval
+    ```