Train the 3D Gaussian Model Using gsplat

1: View the Model Using the gsplat Viewer

To train the 3D Gaussian Splatting model, run the following commands:

conda activate gsplat
cd <GSPLAT_REPOSITORY_FOLDER>\examples
python simple_trainer.py default ^
  --eval_steps -1 ^
  --disable_viewer ^
  --data_factor 4 ^
  --save_ply ^
  --ply_steps 30000 ^
  --data_dir <DATA_PATH> ^
  --result_dir <OUTPUT_PATH>

Replace <GSPLAT_REPOSITORY_FOLDER> with the path to your cloned gsplat repository. Also update <DATA_PATH> and <OUTPUT_PATH> with the correct paths on your local system.

Parameter reference

default: Uses the default configuration profile in simple_trainer.py
--eval_steps -1: Disables evaluation during training
--disable_viewer: Disables the built-in Nerfstudio viewer
--data_factor 4: Downsamples input images by a factor of 4
--save_ply: Enables saving the output model in PLY format
--ply_steps 30000: Saves the .ply file at training step 30,000
--data_dir: Path to the dataset directory (same as used in COLMAP)
--result_dir: Output folder for training results

Expected output

If training starts successfully, you will see a progress bar. Upon completion, the output will include a message like:

Step:  29999 {'mem': 0.2915501594543457, 'ellipse_time': 708.7276656627655, 'num_GS': 167459}
Saving ply to <OUTPUT_PATH>/ply/point_cloud_29999.ply

In the example above, training took approximately 12 minutes on the system described in the tested setup. The resulting model contains 167,459 Gaussians. The final .ply file is saved at:

<OUTPUT_PATH>\ply\point_cloud_29999.ply

You can download the model trained on the sample dataset (without any post-processing or artifact removal) from the repository: results/plush-dog/ply/point_cloud_29999.ply

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This section is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

1 - View the Model Using the gsplat Viewer

To visualize the trained model using gsplat’s built-in viewer, run the following command:

python simple_viewer.py --ckpt <OUTPUT_PATH>\ckpts\ckpt_29999_rank0.pt --port 8081

This launches a local web server and displays a link like the following:

╭──────────────── viser ────────────────╮
│             ╷                         │
│   HTTP      │ http://localhost:8081   │
│   Websocket │ ws://localhost:8081     │
│             ╵                         │
╰───────────────────────────────────────╯

Open the displayed link (for example, http://localhost:8081) in your web browser.

The viewer renders the trained Gaussian Splatting model. Floating artifacts often appear around the main object. The next section, Edit a 3D Gaussian Splatting Model in SuperSplat, shows how to clean and post-process the model for better visualization or downstream use.

Floating artifacts in the Gaussian Splatting model, rendered in the gsplat viewer. These artifacts typically surround the scene before cleanup.

The reconstructed plush toy appears at the center of the same scene when viewed up close.

© 2025 SmartDataScan.
This section is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.