UniqAI/gaussian-splatting
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@ -92,7 +92,7 @@ conda env create --file environment.yml --prefix <Drive>/<env_path>/gaussian_spl
conda activate <Drive>/<env_path>/gaussian_splatting
```
If you can afford the disk space, we recommend using our environment files for setting up a training environment identical to ours. If you want to make changes, please note that major version changes might affect the results of our method. However, our (limited) experiments suggest that the codebase works just fine inside a more up-to-date environment (Python 3.8, PyTorch 2.0.0, CUDA 11.8).
If you can afford the disk space, we recommend using our environment files for setting up a training environment identical to ours. If you want to make modifications, please note that major version changes might affect the results of our method. However, our (limited) experiments suggest that the codebase works just fine inside a more up-to-date environment (Python 3.8, PyTorch 2.0.0, CUDA 11.8).
### Running
@ -181,7 +181,7 @@ python render.py -m <path to trained model> # Generate renderings
python metrics.py -m <path to trained model> # Compute error metrics on renderings
```
If you want to evaluate our pre-trained models, you will have to download the corresponding source data sets and indicate their location to ```render.py``` with an additional ```--source_path/-s``` flag. Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bugfixes, hence the metrics you get from evaluating them will differ from those in the paper.
If you want to evaluate our [pre-trained models](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip), you will have to download the corresponding source data sets and indicate their location to ```render.py``` with an additional ```--source_path/-s``` flag. Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bugfixes, hence the metrics you get from evaluating them will differ from those in the paper.
```shell
python render.py -m <path to pre-trained model> -s <path to COLMAP dataset>
python metrics.py -m <path to pre-trained model>
@ -400,6 +400,7 @@ If you have your own COLMAP dataset without undistortion (e.g., using ```OPENCV`
|---distorted
| |---sparse
| | |---0
| | | |---...
| |---database.db
```
Then run