| --- |
| 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 路 Simon Julier** |
| University College London |
|
|
| [Paper](https://arxiv.org/abs/2610.28490) 路 [Code](https://github.com/changruizhu96/LEGO-SCD) 路 [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} |
| } |
| ``` |
|
|
