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GaussianSplatting2D

A Python library for image approximation via adaptive 2D Gaussian splatting. Iteratively optimizes a set of splats to reconstruct a target image with minimal memory footprint and allows quantization for shader use. Training Result

  • 500 Splats after 1000 Epochs

Features

  • Adaptive sample splitting and scaling based on gradient magnitude
  • Quantized export for ShaderToy as uvec4 arrays
  • Checkpointing and multi-bit quantization of splat parameters

Example Shader

https://www.shadertoy.com/view/Wc33W4

Installation

Install PyTorch

pip install -r requirements.txt

Usage

  1. Place your target image in the project root.

  2. Adjust parameters in the __main__ section of main.py if needed (epochs, sample count, thresholds).

  3. Run training:

    python main.py

  4. Interrupt with Ctrl+C to save the latest checkpoint.

  5. Automatically copies splat parameters to clipboard, for use in ShaderGraph

Configuration Parameters

  • num_samples – Initial splat count
  • num_max_samples – Maximum allowed splats
  • sigma_thre – Minimum standard deviation for splitting
  • grad_thre – Gradient threshold for adaptive refinement
  • num_epoch – Total training epochs
  • num_iter_per_epoch – Iterations per epoch

Output

  • Checkpoints saved under training/<image_basename>/<image_basename>.pt
  • Quantized variants:
    • 16-bit-quantized-<name>.pt
    • 8-bit-quantized-<name>.pt
  • Reconstruction and target side-by-side images in training/<name>/images/
  • Clipboard export of uvec4 arrays for ShaderToy

License

This project is released under the MIT License.