UniqAI/gaussian-splatting
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@ -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. Add this flag to use a MipNeRF360-style training/test split for evaluation.
#### --resolution / -r #### --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.** 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 #### --white_background / -w
Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset. Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.
#### --sh_degree #### --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. Space-separated iterations at which the training script saves the Gaussian model, ```7000 30000 <iterations>``` by default.
#### --quiet #### --quiet
Flag to omit any text written to standard out pipe. 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.
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