UniqAI/gaussian-splatting
1
0
Fork 0
mirror of https://github.com/graphdeco-inria/gaussian-splatting synced 2024-11-22 08:18:17 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Snosixtyboo 2023-07-12 21:25:21 +02:00 committed by GitHub
parent 8abb25b632
commit d843dc1224
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -115,6 +115,8 @@ python train.py -s <path to COLMAP or NeRF Synthetic dataset>
Add this flag to use a MipNeRF360-style training/test split for evaluation.
#### --resolution / -r
Specifies resolution of the loaded images before training. If provided ```1, 2, 4``` or ```8```, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. **If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target.**
#### --data_device
Specifies where to put the source image data, ```cuda``` by default, recommended to use ```cpu``` if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training.
#### --white_background / -w
Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.
#### --sh_degree
@ -165,8 +167,6 @@ python train.py -s <path to COLMAP or NeRF Synthetic dataset>
Space-separated iterations at which the training script saves the Gaussian model, ```7000 30000 <iterations>``` by default.
#### --quiet
Flag to omit any text written to standard out pipe.
#### --data_device
Specify where to put the source image data, ```cuda``` by default, recommended to use ```cpu``` if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training.
</details>
<br>