Teleeeeeeeeeee/LEGO-SCD

Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change Detection

Changrui Zhu · Simon Julier
University College London

Paper · Code · Project page

This repository hosts the released scene-specific fitted parameters for LEGO-SCD. The method learns statistical models from automatic reference 3D Gaussian Splatting edits and unchanged reference observations, then combines current-view evidence with accumulated history for online scene change detection.

Contents

Dataset	Parameter directory	Coverage
PASLCD	pretrained/PASLCD/Instance_<1 or 2>/<scene>/	20 scene-instances
CCS3D	pretrained/CCS3D/<scene>/	bedroom, bookcase, desk, livingroom

Download pretrained.zip and extract it to recover the directory layout above. The uncompressed parameter files total approximately 88 MB. Each scene-instance contains:

• suppression.pt: fitted response suppressor.
• likelihood.json: image evidence mapping.
• unchanged.pt: unchanged-response statistics for historical evidence accumulation.
• prior.json: effective pixel prior.
• contour.pkl: fitted contour filter for refinement.
• split.json: reference-view split information.

These parameters match the reference reconstructions used by the released code. Unchanged-response statistics are indexed by Gaussian: replacing, pruning, or reordering a reconstruction requires fitting new parameters. A new scene also requires its own offline fitting. See the code repository for data preparation and training instructions.

Use with the released code

Install LEGO-SCD and prepare the dataset following the GitHub README. The GitHub repository already includes these parameters; this Hub repository provides an additional download source.

To download this mirror into your LEGO-SCD checkout, run the following from that checkout in an environment with huggingface_hub installed:

from huggingface_hub import hf_hub_download
from zipfile import ZipFile

archive = hf_hub_download(
    repo_id="Teleeeeeeeeeee/LEGO-SCD",
    filename="pretrained.zip",
)
with ZipFile(archive) as parameters:
    parameters.extractall(".")

After installation and dataset preparation, use the existing evaluation entry:

python run_evaluation.py PASLCD
python run_evaluation.py CCS3D

Add --pixel-only to run without output refinement. The code repository includes fixed initialization edits and documents online timing separately.

The input datasets, reference reconstructions, and SAM2/XFeat weights are obtained separately through the code repository's preparation instructions. This Hub repository contains the fitted parameters, not the full inference environment.

License and attribution

The project contributors' rights in these fitted parameters are licensed under CC BY 4.0. See the parameter license and the CCS3D notice for attribution and upstream sources. The source datasets, reference reconstructions, and third-party code and weights retain their respective licenses.

Citation

@article{zhu2026learning,
  title={Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change Detection},
  author={Zhu, Changrui and Julier, Simon},
  journal={arXiv preprint arXiv:2610.28490},
  year={2026},
  url={https://arxiv.org/abs/2610.28490}
}