Tinysplat is a minimal 3D Gaussian splatting implementation aiming to reach SOTA performance in training speed and accuracy on few-view indoor training tasks. It currently leverages the gsplat library developed by members of the Nerfstudio team (🙏). Tinysplat was originally written to use Tinygrad, but has since switched to PyTorch due to poor ergonomics of custom CUDA kernels in Tinygrad.

Notable features:

• Depth-guided splat regularization (in the manner of Chung et al, 2023)
• Density regularization and mesh extraction (in the manner of Guédon & Lepetit, 2023)*
• Real-time browser-based scene viewer
• Image undistortion

Upcoming features:

• Diffusion-guided splat regularization (inspired by Reconfusion)

1. Prepare a dataset for 3D reconstruction, for example the SfM-processed Tanks and Temples dataset provided by INRIA's FUNGRAPH here.

2. Start the training procedure:

   LOG_LEVEL=DEBUG python scripts/train.py --train --regularize-depth --dataset-dir=datasets/truck

   A full list of the available options can be displayed with python scripts/train.py --help.

3. View the scene during training with a freely moving camera by launching the viewer:

   cd viewer; npx vite

   You can now navigate to http://localhost:5173, using the WASD+QE keys and mouse to explore.

_{* Disclaimer: Some bugs may still be present}