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https://github.com/uc-vision/splat-viewer
A viewer and other tools for the taichi-splatting rasterizer.
https://github.com/uc-vision/splat-viewer
Last synced: 3 months ago
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A viewer and other tools for the taichi-splatting rasterizer.
- Host: GitHub
- URL: https://github.com/uc-vision/splat-viewer
- Owner: uc-vision
- License: apache-2.0
- Created: 2024-01-02T15:02:56.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-29T16:21:00.000Z (4 months ago)
- Last Synced: 2024-07-29T22:02:35.180Z (4 months ago)
- Language: Python
- Size: 120 KB
- Stars: 18
- Watchers: 5
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-3D-gaussian-splatting - uc-vision-splat-viewer
README
# Overview
A viewer and some tools to work with guassian splatting reconstructions. Currently will open .ply files and gaussian splatting workspaces from the original guassian-splatting implementation. Intended primarily for testing [taichi-splatting](https://github.com/uc-vision/taichi-splatting)
# Example data
Some example scenes can be found from the official [gaussian-splatting](https://github.com/graphdeco-inria/gaussian-splatting) page. Under [Pre-trained models](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip).
## Installing
### External dependencies
Create an environment (for example conda with mambaforge) with the following dependencies:
* python >= 3.10
* pytorch - from either conda Follow instructions [https://pytorch.org/](here).
* taichi-nightly `pip install --upgrade -i https://pypi.taichi.graphics/simple/ taichi-nightly`
### Install
One of:
* `pip install splat-viewer`
* Clone down with `git clone` and install with `pip install ./splat-viewer`
# splat-viewer
A gaussian splatting viewer. An example of some visualizations produced by this viewer can be seen:
[](https://www.youtube.com/embed/4ysMY5lti7c)
## Arguments
```
usage: splat-viewer [-h] [--model MODEL] [--device DEVICE] [--debug] model_path
positional arguments:
model_path workspace folder containing cameras.json, input.ply and point_cloud folder with .ply models
options:
-h, --help show this help message and exit
--model MODEL load model from point_clouds folder, default is latest iteration
--device DEVICE torch device to use
--debug enable taichi kernels in debug mode
```
## Keyboard Controls
### Switch View mode
* 1: normal rendering
* 2: render gaussian centers as points
* 3: render depth map
### Show/hide
* 0 : cropped foreground
* 9 : initial points
* 8 : camera markers
### Misc
* prntsc: save high-res snapshot into workspace directory
* shift return: toggle fullscreen
### Camera
* '[' : Prev camera
* ']' : Next camera
* '=' : zoom in
* '-' : zoom out
* w/s a/d q/e : forward/backward left/right up/down
* keypad plus/minus: navigate faster/slower
### Animation
* space: add current viewpoint to animaiton sequence
* control-space: save current animation sequence to workspace folder
* return: animate current sequence
* shift plus/minus: animation speed faster/slower
# splat-benchmark
A benchmark to test forward and backward passes of differentiable renderers.
Example `splat-benchmark models/garden --sh_degree 1 --image_size 1920`
## Arguments
```
usage: splat-benchmark [-h] [--device DEVICE] [--model MODEL] [--profile] [--debug] [-n N] [--tile_size TILE_SIZE] [--backward] [--sh_degree SH_DEGREE] [--no_sort] [--depth]
[--image_size RESIZE_IMAGE] [--taichi]
model_path
positional arguments:
model_path workspace folder containing cameras.json, input.ply and point_cloud folder with .ply models
options:
-h, --help show this help message and exit
--device DEVICE torch device to use
--model MODEL model iteration to load from point_clouds folder
--profile enable profiling
--debug enable taichi kernels in debug mode
-n N number of iterations to render
--tile_size TILE_SIZE
tile size for rasterizer
--backward benchmark backward pass
--sh_degree SH_DEGREE
modify spherical harmonics degree
--no_sort disable sorting by scale (sorting makes tilemapping faster)
--depth render depth maps
--image_size RESIZE_IMAGE
resize longest edge of camera image sizes
--taichi use taichi renderer
```