Awesome

Simple Viser Viewer for 2D Gaussian Splatting for Geometrically Accurate Radiance Fields

2D GS Project page | Paper | Original Github <br>

This repo contains the unofficial viewer for the "2D Gaussian Splatting for Geometrically Accurate Radiance Fields". A significant portion of this project is built upon several existing works to provide enhanced visualization and editing capabilities for 2D Gaussian Splatting data.

• Related Blog Post: Review of 2D Gaussian Splatting

⭐ Features

• Various Render Type: RGB / Edge / Normal / View-Normal / Depth / Depth-to-Normal / Depth-Distortion / Curvature
• Disk Visualization
• Edit & Save Splats
• Mesh Export (in Edit Tab)
• Render Path and Preview

Updates History

• 2024/06/28
  • Disk Visualization
  • Fix camera parameters to the same as the original work
  • Fix CUDA device side assertion error when color mapping (divide by zero)
• 2024/06/21
  • Mesh Export in Edit Tab: you can export the mesh of the scene (also for the edited scene) and show the result to the viewer
• 2024/06/17
  • Improve fps
  • Minor code revision
• 2024/06/07
  • Render Types Update
    • Now supports Edge/Curvature/Depth-Distortion render type
      • The edge/curvature visualization is inspired by Gaussian Splatting Monitor.
    • For one channel output image (e.g., depth), use the 'Turbo' color map for the better visualization
    • Note. Depth-distortion visualization is quite interesting. In the early stages of training, it shows noisy and misaligned splats, but after training, splats align along the depth, highlighting 'edges' on the view frustum.
  • Bug Fixes
    • Various render options are available during training
    • Transform after editing
• 2024/06/05
  • Training / Rendering Features
    • Now supports training with a viewer (large memory consumption)
    • Supports render path generation & preview camera paths
  • Minor code revision
    • Add 'Set to default' in the transform panel
    • Cropbox w/ multi slider
    • Edit mode visualizes only pointcloud for clarity
    • Fix negative parts truncation in normal rendering
• 2024/06/03
  • General Features
    • Supports various render types including Normal / Depth / Depth2Normal
    • Direct comparison between different render types, e.g., normal vs depth-to-normal
    • Cropbox Region
    • Pointcloud visualization
  • Editing Features
    • Edit, delete and save point clouds (Recommended with: 'Add pointcloud')
  • Transform Features
    • Rigid Transform
• 2024/05/31
  • Viewer release

Installation

• If you already have the conda environment of 2D GS, then use it
• If not, follow the installation instruction from the original 2D GS

git clone https://github.com/hwanhuh/2D-GS-Viser-Viewer.git --recursive
cd 2D-GS-Viser-Viewer
pip install viser==0.1.29
pip install splines  
pip install lightning

Usage

• View a 2D GS ply file

python viewer.py <path to pre-trained model> <or direct path to the ply file> -s <data source path>
### enable transform mode
python viewer.py <path to pre-trained model> -s <data source path> --enable_transform

• Train w/ viewer

python train_w_viewer.py -s <path to datas>

• Colab
  • You can also use the viewer in the Colab, powered by ngrok (see example)
  • To use Colab and ngrok, you should add the below code to the 'start' function in the 'viewer.py'

    def start(self, block: bool = True, server_config_fun=None, tab_config_fun=None):
        # create viser server
        server = viser.ViserServer(host=self.host, port=self.port)
        self._setup_titles(server)
        if server_config_fun is not None:
            server_config_fun(self, server)

        ### attach here!!!
        from pyngrok import ngrok
        authtoken = "your authtoken"
        ngrok.set_auth_token(authtoken)
        public_url = ngrok.connect(self.port)
        print(f"ngrok tunnel URL: {public_url}")
        ### 

Control

• 'q/e' for up & down
• 'w/a/s/d' for moving
• Mouse wheel for zoom in/out

Acknowledgements

This project is built upon the following works

• Original 2D GS Github
• Viser
• Gaussian Splatting Pytorch Lightning.

Related Blog Post: Review of 2D Gaussian Splatting