UniqAI/gaussian-splatting
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mirror of https://github.com/graphdeco-inria/gaussian-splatting synced 2024-11-25 13:26:47 +00:00
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Bumped SIBR, documentation, always save at end

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bkerbl 2023-07-04 15:10:44 +02:00
parent 3982254416
commit 25c9817c49
3 changed files with 9 additions and 2 deletions
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7
README.md
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@ -71,8 +71,13 @@ Our provided install method is based on Conda package and environment management
conda env create --file environment.yml conda env create --file environment.yml
conda activate gaussian_splatting conda activate gaussian_splatting
``` ```
Note for Windows: if setup stops and you are prompted to set ```DISTUTILS_USE_SDK=1```, please complete the environment setup with:
```shell
SET DISTUTILS_USE_SDK=1
conda env update --file environment.yml
```
Note that downloading packages and creating a new environment with Conda can require a significant amount of disk space. By default, Conda will use the main system hard drive. You can avoid this by specifying a different package download location and an environment on a different drive: Tip: Downloading packages and creating a new environment with Conda can require a significant amount of disk space. By default, Conda will use the main system hard drive. You can avoid this by specifying a different package download location and an environment on a different drive:
```shell ```shell
conda config --add pkgs_dirs <Drive>/<pkg_path> conda config --add pkgs_dirs <Drive>/<pkg_path>

2
SIBR_viewers_windows

@ -1 +1 @@
Subproject commit eb1b8513ecc0cdb1dfbd9dae4e0fe64c9dcef2ab Subproject commit 0a1620ace38f1d26e685312a533cc95064902c15

2
train.py
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@ -180,6 +180,8 @@ if __name__ == "__main__":
parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000]) parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
parser.add_argument("--quiet", action="store_true") parser.add_argument("--quiet", action="store_true")
args = parser.parse_args(sys.argv[1:]) args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
print("Optimizing " + args.model_path) print("Optimizing " + args.model_path)
# Initialize system state (RNG) # Initialize system state (RNG)