# LayoutVLM

## Installation 1. Clone this repository 2. Install dependencies (python 3.10): ```bash pip install -r requirements.txt ``` 3. Install Rotated IOU Loss (https://github.com/lilanxiao/Rotated\_IoU) ``` cd third_party/Rotated_IoU/cuda_op python setup.py install ```` ## Data preprocessing 1. Download the dataset https://drive.google.com/file/d/1WGbj8gWn-f-BRwqPKfoY06budBzgM0pu/view?usp=sharing 2. Unzip it. Refer to https://github.com/allenai/Holodeck and https://github.com/allenai/objathor for how we preprocess Objaverse assets. ## Usage 1. Prepare a scene configuration JSON file of Objaverse assets with the following structure: ```json { "task_description": ..., "layout_criteria": ..., "boundary": { "floor_vertices": [[x1, y1, z1], [x2, y2, z2], ...], "wall_height": height }, "assets": { "asset_id": { "path": "path/to/asset.glb", "assetMetadata": { "boundingBox": { "x": width, "y": depth, "z": height } } } } } ``` 2. Run LayoutVLM: ```bash python main.py --scene_json_file path/to/scene.json --openai_api_key your_api_key ``` ## Output The script will generate a layout.json file in the specified save directory containing the optimized positions and orientations of all assets in the scene. ## BibTeX ```bibtex @inproceedings{sun2025layoutvlm, title={Layoutvlm: Differentiable optimization of 3d layout via vision-language models}, author={Sun, Fan-Yun and Liu, Weiyu and Gu, Siyi and Lim, Dylan and Bhat, Goutam and Tombari, Federico and Li, Manling and Haber, Nick and Wu, Jiajun}, booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference}, pages={29469--29478}, year={2025} } ```