Yuheng02/OmniRoam-InteriorGS-Path

OmniRoam - InteriorGS Rendering Pipeline

A batch rendering tool for rendering panoramic videos from 3D Gaussian Splatting (3DGS) datasets using Blender.

Prerequisites

System Requirements

• Linux (tested on Ubuntu 20.04+)
• GPU with CUDA support (recommended for faster rendering)
• Python 3.11 (included with Blender 4.5.6)
• Node.js >= 18.0.0
• FFmpeg

Required Software

1. Blender 4.5.6 (Linux x64)
2. Gaussian Splatting Blender Addon
3. splat-transform (npm package for PLY decompression)
4. Python packages: tqdm, scipy, huggingface_hub

Installation

Step 1: Install Blender 4.5.6

wget https://www.blender.org/download/release/Blender4.5/blender-4.5.6-linux-x64.tar.xz

tar -xf blender-4.5.6-linux-x64.tar.xz

This will create a blender-4.5.6-linux-x64 directory containing Blender.

Step 2: Install Gaussian Splatting Blender Addon

git clone git@github.com:ReshotAI/gaussian-splatting-blender-addon.git

cd gaussian-splatting-blender-addon
./zip.sh

This creates blender-addon.zip in the addon directory.

Move the addon to your project root:

cd ..
mkdir -p gaussian-splatting-blender-addon
cp gaussian-splatting-blender-addon/blender-addon.zip gaussian-splatting-blender-addon/

Step 3: Install Python Dependencies

The script will automatically install required Python packages (tqdm, scipy) to Blender's Python environment on first run. To manually install:

./blender-4.5.6-linux-x64/4.5/python/bin/python3.11 -m pip install tqdm scipy huggingface_hub

Step 4: Install Node.js and splat-transform

The script will attempt to install Node.js via nvm if not present. To manually install:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.7/install.sh | bash
source ~/.bashrc
nvm install 20
nvm use 20

npm install -g @playcanvas/splat-transform

Step 5: Project Structure

Ensure your project directory has this structure:

your-project/
├── blender-4.5.6-linux-x64/
│   └── blender
├── gaussian-splatting-blender-addon/
│   └── blender-addon.zip
├── render.py
├── run_simple.sh
├── valid_datasets.txt
├── datasets/              (will be created automatically)
└── output/                (will be created automatically)

Dataset Download

The dataset files come from two separate Hugging Face repositories and require manual setup.

Dataset Sources

1. 3D Gaussian Splatting (3DGS) Files:

   • Repository: spatialverse/InteriorGS (private, requires HF token)
   • File: 3dgs_compressed.ply (will auto-download and decompress)

2. Camera Path Files:

   • Repository: Yuheng02/OmniRoam-InteriorGS-Path
   • File: path_3.zip (requires manual download and extraction)

Setup Instructions

Step 1: Configure Hugging Face Token

The InteriorGS dataset is private. You need to:

1. Get your Hugging Face token from https://huggingface.co/settings/tokens
2. Edit render.py and set your token:

   HF_TOKEN = "hf_xxxxxxxxxxxxxxxxxxxxx"  # Your token here
   

Or provide it via command line:

./blender-4.5.6-linux-x64/blender --background --python render.py -- \
    --dataset-file valid_datasets.txt \
    --hf-token hf_xxxxxxxxxxxxxxxxxxxxx

Step 2: Download Camera Path Files

The camera trajectories must be downloaded manually:

1. Download from Hugging Face:

   # Visit: https://huggingface.co/datasets/Yuheng02/OmniRoam-InteriorGS-Path
   # Download path_3.zip
   wget https://huggingface.co/datasets/Yuheng02/OmniRoam-InteriorGS-Path/resolve/main/path_3.zip
   

2. Extract to datasets directory:

   unzip path_3.zip -d datasets/
   

   This will create datasets/<dataset_id>/path.json for each dataset.

Usage

Run Rendering

Single Process Mode

Process all datasets sequentially:

./blender-4.5.6-linux-x64/blender --background --python render.py -- --dataset-file valid_datasets.txt

Multi-Process Parallel Mode

Process multiple splits in parallel using the shell script:

./run_simple.sh 1 2 3 4 5 200

This command:

• Processes splits 1, 2, 3, 4, 5 (out of 200 total splits)
• Uses 5 parallel processes
• Each process handles its assigned subset of datasets

Manual Python Command

For more control, use the Python script directly:

./blender-4.5.6-linux-x64/blender --background --python render_clean.py -- \
    --dataset-file valid_datasets.txt \
    --split 1 2 3 \
    --total-split 200

Command Line Options

--datasets ID1 ID2 ...        Dataset ID list (space separated)
--dataset-file FILE           Text file with dataset IDs (one per line)
--base-path PATH              Local dataset cache path (default: datasets)
--output-dir PATH             Output directory (default: output)
--target-frames N             Target video frame count (default: 800)
--hf-token TOKEN              Hugging Face token (for private repos)
--split N1 N2 ...             Split numbers to process
--total-split N               Total number of splits
--dry-run                     Validate datasets only, don't render
--help, -h                    Show help message

Output

For each dataset, the rendering pipeline produces:

output/<dataset_id>/
├── pano_camera0/
│   ├── frame_0001.png    (PNG image sequence)
│   ├── frame_0002.png
│   ├── ...
│   └── frame_0800.png
├── video.mp4              (Compiled video, 30fps)
└── transforms.json        (Camera positions and transformations)

Output Files

1. PNG Sequence (pano_camera0/frame_*.png)

   • Format: PNG, RGB, 8-bit
   • Resolution: 1920x960 (configurable)
   • Compression level: 15
   • Count: 800 frames (default)

2. Video (video.mp4)

   • Codec: H.264 (libx264) with CRF 18
   • Fallback: libopenh264 or mpeg4
   • Frame rate: 30 fps
   • Pixel format: yuv420p

3. Camera Data (transforms.json)

   • Camera-to-world transformation matrices (4x4)
   • Camera positions (world coordinates)
   • Camera rotations (Euler angles)
   • Format: OpenCV/Nerfstudio compatible

transforms.json Structure

{
  "coordinate_convention": "OpenCV: x_cam = R * X_world + t (Rcw/tcw)",
  "twc_convention": "Nerfstudio: X_world -> cam with inverse(Twc); stored transform_matrix = Twc (cam-to-world)",
  "dataset_id": "0001_839920",
  "num_images": 800,
  "per_image": {
    "pano_camera0/frame_0001.png": {
      "transform_matrix": [[...], [...], [...], [...]],
      "location": [x, y, z],
      "rotation": [rx, ry, rz],
      "frame": 1
    },
    ...
  }
}

Configuration

Edit these variables in render.py for customization:

dataset_base_path = "datasets"          # Local dataset cache
output_base_dir = "output"              # Output directory
target_frames = 800                     # Video length
fps = 30                                # Frame rate
target_speed = 0.02                     # Camera movement speed (m/frame)

Render settings (resolution, samples, etc.) can be modified in setup_render_settings().

Acknowledgments

• Blender Foundation
• ReshotAI Gaussian Splatting Addon
• PlayCanvas splat-transform
• InteriorGS Dataset
• OmniRoam-InteriorGS-Path

License

The codebase is released under the Adobe Research License for noncommercial research purposes only.