See some example editings in this video.
NEW UPDATE: splatviz now supports live tracking of a running 3DGS training: video.
| 1. Download |
Clone repository recursively in order to install glm from the diff_gaussian_rasterization package.
git clone https://github.com/Florian-Barthel/splatviz.git --recursive |
|
|---|---|---|
| 2. Install |
Create conda environment with conda:
conda env create \
-f environment.yml
conda activate gs-view |
Alternatively, create the environment with micromamba:
micromamba env create \
--file environment.yml \
--channel-priority flexible -y
micromamba activate gs-view |
| Mode | Description | Command |
|---|---|---|
| Default Mode | The default mode loads and renders standard 3DGS scenes stored in ply files or compressed yml files. |
python run_main.pyor launch with a specified path to look for .ply or .yml files: python run_main.py --data_path=path/with/ply/files |
| Attach to Training | The _attach_ mode allows you to view and edit a running 3DGS training. This works with the default implementation of 3DGS. In order to edit the scene during the training and to view some extra information, follow the simple steps described here: https://github.com/Florian-Barthel/splatviz_network. |
python run_main.py --mode=attach |
| Decoder Mode | The decoder mode renders human heads generated by 3D GANs from this paper. |
python run_main.py \
--mode=decoder \
--data_path=path/with/pkl/files \
--ggd_path=path/to/gaussian_gan_decoder/project |
Once you run the run_main.py file, the viewer will directly load the first scene that is found in the data
directory you have specified. You can change the scene by clicking the Browse button. You will be displayed a list
of available .ply files (or .yml files that correspond to compressed gaussian scenes using
this compression method). If you have a lot of
scenes to chose from, you can also use the Filter textfield providing comma separated keywords (eg.
lego,30000,baseline).
You will then only see those scenes that contain all keywords.
Additionally, you can also view multiple 3D scenes at once. Simply click Add Scene, which loads the same scene as before and then change the respective scene to another .ply file using the new browse button. The scenes are either rendered next to each other or in a split screen mode when activating the Splitscreen checkbox.
You can download some example scenes here. They were created using the gaussian splatting compression method from: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians.
The edit widget is the core functionality of this 3D viewer. It allows for real time editing of the gaussian python object during runtime. The code that you type in this text area will be executed just before the gaussian object is forwarded to the cuda renderer. This means, the editing capabilities are unlimited. As long as the provided code is executable, you can type any kind of python code and also import new libraries. An example could look like this, where all scales are set to -8 (before activating with exp()) and all opacity values are set to 10 (before activating with sigmoid()), while also the background is set to 1, which corresponds to white.
gaussian._scaling = gaussian._scaling * 0 - 8
gaussian._opacity = gaussian._opacity * 0 + 10
self.bg_color[:] = 1To enable smooth editing transitions, you can create sliders (press Add Slider) which you can access in the editor text by typing slider.name (eg. slider.x). An example could look as follows. Here, we create a boolean mask that filters all gaussians that are smaller than the value stored in slider.x.
mask = torch.linalg.norm(gaussian._scaling, dim=-1) < slider.x
gaussian._xyz = gaussian._xyz[mask]
gaussian._rotation = gaussian._rotation[mask]
gaussian._scaling = gaussian._scaling[mask]
gaussian._opacity = gaussian._opacity[mask]
gaussian._features_dc = gaussian._features_dc[mask]
gaussian._features_rest = gaussian._features_rest[mask]Lastly, you can save and load presets of code snippets so that you don't have to type the same code again after closing the application. Those presets are stored in a .json file (presets.json).
The evaluate widget can be used to debug the gaussian splatting object. By typing python code, which is executed after the rendering, you can access any variable from the rendering context and visualize them in a histogram. Some useful variables might be:
- gaussian
- render
- render_cam
- self
You can also access variables that you have defined in the editor of the Edit Widget.
In the camera widget you can define the type and parameters of the camera. Most importantly, you can choose between the two modes Orbit and WASD.
In Orbit mode, the camera is looking at a specific point in 3D space, and you control the pitch and yaw of the camera rotating on a sphere around that point by dragging with the mouse over the rendered view.
In WASD mode, you fly through the scene using the mouse and the WASD keys similar to the camera controls in Unity.
Important: If the loaded scene is rotated incorrectly, you can adjust that by steering the camera so that it is looking straight up or down. Then set the up-vector to the current viewing direction by pressing **Set Current Direction **. If you were looking down, you will have to press the Flip button, since the scene is now upside down.
The video widget creates a video sequence of a full rotation around the current object. Simply define the height of the camera and the rendering resolution. While the video is rendering, the UI is frozen. A loading screen is shown in the terminal output.
🚧This feature is still under construction 🚧. Currently, it is not very intuitive to generate videos, as the camera position is only defined by the Camera Height parameter. This will be changed in the future.
In the performance widget, you can track the FPS of the viewer and the FPS of the renderer. You can also specify limits for FPS and enable vertical synchronisation. In the future, there will also be a performance mode which skips some editing and evaluation calculations.
In the render tab, you can specify the rendering resolution (at the moment only squared resolutions) and the rendering mode. Next to RGB, you can also render the image 3D depth and the alpha values.
Version 1.1.0
- 2024-08-12: Added a new Training Widget to inspect live training stats and to pause training
- 2024-08-11: Attach to a running 3DGS training
- 2024-08-10: Refactor rendering class for easier addition of new renderer
- 2024-08-07: Better Mouse Control (translate with middle mouse button)
- 2024-08-05: Allow editing of existing sliders
- 2024-07-30: Store slider values in presets
- 2024-07-28: New Performance Widget
- 2024-07-28: Editor now highlights special variables (gs, self, slider) and gives tooltips
Version 1.0.0
- 2024-07-12: Rebuild the whole application with imgui_bundle
- 2024-07-05: Compare two or more Gaussian Splatting scenes side by side
- Test other 3DGS render for OSX support
- Improve the Video Widget
- Tutorial for coding new widgets or renderer
You are more than welcome to add further functionality or a better design to this interactive viewer! The main goal is to create an easy-to-use tool that can be applied for debugging and for understanding 3D Gaussian Splatting objects. For reformating code please use black with --line-length 120.
If you find this viewer useful, please consider citing our work:
@misc{barthel2024gaussian,
title={Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks},
author={Florian Barthel and Arian Beckmann and Wieland Morgenstern and Anna Hilsmann and Peter Eisert},
year={2024},
eprint={2404.10625},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
This viewer is inspired by the visualizer from Efficient Geometry-aware 3D Generative Adversarial Networks (EG3D).
- GUI: pyimgui and imgui_bundle which are python wrappers for the c++ library ImGUI
- Original code base: EG3D
- 3DGS: 3D Gaussian Splatting
- Compressing 3DGS scenes: Compact 3D Scene Representation via Self-Organizing Gaussian Grids
- 3DGS Rasterizer with depth and alpha: Diff rasterizer with depth and alpha