Release LEGO-SCD scene-specific fitted parameters and model card

CCS3D_LICENSE.md

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+# CCS3D fitted parameters
+
+The project contributors' rights in these fitted parameters are licensed under
+CC BY 4.0, as specified in the [fitted parameter license](LICENSE.md).
+
+Source data: [CCS3D](https://huggingface.co/datasets/zr-zhou/CCS3D) and
+[reference reconstructions](https://huggingface.co/zr-zhou/CCS3D-pretrained),
+provided by the [SCaR-3D authors](https://github.com/zr-zhou0o0/SCaR-3D).
+These upstream assets are obtained separately and retain their own terms.

LICENSE.md

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+# Fitted parameter license
+
+The project contributors' rights in the fitted parameters under `PASLCD/` and `CCS3D/`
+are licensed under [Creative Commons Attribution 4.0 International
+(CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode).
+
+Attribution: Changrui Zhu and Simon Julier, *Learning from Automatic 3D Gaussian
+Splatting Edits for Online Bayesian Scene Change Detection*, 2026.
+[Paper](https://arxiv.org/abs/2610.28490) ·
+[Source](https://github.com/changruizhu96/LEGO-SCD)
+
+These parameters were fitted from automatic edits to the reference scenes of
+[PASLCD](https://huggingface.co/datasets/ChamudithaJay/PASLCD), by Chamuditha
+Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko
+Suenderhauf, and Dimity Miller, and
+[CCS3D](https://huggingface.co/datasets/zr-zhou/CCS3D), by Zirui Zhou, Junfeng Ni,
+Shujie Zhang, Yixin Chen, and Siyuan Huang
+([SCaR-3D](https://github.com/zr-zhou0o0/SCaR-3D)).
+
+The original datasets and reference reconstructions are obtained separately from
+their respective authors. They and SAM2/XFeat weights are not included in these
+checkpoints and retain their own terms.

README.md

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 ---
 license: cc-by-4.0
+language:
+- en
+tags:
+- computer-vision
+- scene-change-detection
+- 3d-gaussian-splatting
+- bayesian-inference
+- scene-specific
+- arxiv:2610.28490
+datasets:
+- ChamudithaJay/PASLCD
+- zr-zhou/CCS3D
 ---
+
+# Learning from Automatic 3D Gaussian Splatting Edits for Online Bayesian Scene Change Detection
+
+**Changrui Zhu · Simon Julier**  
+University College London
+
+[Paper](https://arxiv.org/abs/2610.28490) · [Code](https://github.com/changruizhu96/LEGO-SCD) · [Project page](https://changruizhu96.github.io/work/lego-scd/)
+
+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](https://github.com/changruizhu96/LEGO-SCD#install).
+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:
+
+```python
+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:
+
+```bash
+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](LICENSE.md) and
+[the CCS3D notice](CCS3D_LICENSE.md) for attribution and upstream sources.
+The source datasets, reference reconstructions, and third-party code and weights
+retain their respective licenses.
+
+## Citation
+
+```bibtex
+@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}
+}
+```

pretrained.zip

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+oid sha256:e59a12367684d5e714f18c2aa09d80e48b95f0a9bf060253ad631feee28c6dcd
+size 38618190