This is a fork of 3D Gaussian Splatting. Refer to the original repo for instructions on how to run the code.

After having installed the 3D Gaussian Splatting code, run the following command:

python create_dataset.py --img_path /path/to/image --output_dir /path/to/output_dir

You can disable the opacity_reset_interval argument by setting it to 30_000.

You can also set sh_degree to 0 to disable viewdependent effects.

This will create a dataset ready to be trained with the Gaussian Splatting code.

• Orthogonal images (using create_dataset2.py)

• Steganography (using create_dataset3.py)

Using the SIBR visualizer, you can visualize the "painting" process during the Gaussian Splatting optimization.

The create_dataset script simply creates a COLMAP output directory with a single camera pointing at a plane. 100 points are sampled from the image and used as initial point cloud for the Gaussian Splatting optimization. A second perpendicular image is also created with a black image as target.