Add model details, paper links and usage information
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by nielsr HF Staff - opened
README.md
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---
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license: cc-by-nc-4.0
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tags:
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- 3d-object-detection
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- open-world-detection
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- 3d-vision
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datasets:
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- facebook/boxer
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---
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---
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datasets:
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- facebook/boxer
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license: cc-by-nc-4.0
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pipeline_tag: object-detection
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tags:
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- 3d-object-detection
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- open-world-detection
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- 3d-vision
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---
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# Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D
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[Project Page](https://facebookresearch.github.io/boxer) | [Paper](https://huggingface.co/papers/2604.05212) | [Code](https://github.com/facebookresearch/boxer)
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Boxer is an algorithm designed to estimate static 3D bounding boxes (3DBBs) from 2D open-vocabulary object detections, posed images, and optional depth data. At its core is **BoxerNet**, a transformer-based network which lifts 2D bounding box (2DBB) proposals into 3D, followed by multi-view fusion and geometric filtering to produce globally consistent de-duplicated 3DBBs in metric world space.
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## Installation
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We recommend using [uv](https://docs.astral.sh/uv/) to manage the environment:
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```bash
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# Create virtual environment
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uv venv boxer --python 3.12
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source boxer/bin/activate
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# Core dependencies for running Boxer
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uv pip install 'torch>=2.0' numpy opencv-python tqdm dill
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```
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## Usage
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After installation and downloading the required checkpoints using the scripts provided in the repository, you can run BoxerNet on sample data. For example, to run BoxerNet in headless mode on a sample sequence:
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```bash
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python run_boxer.py --input nym10_gen1 --max_n=90 --track
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```
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This will estimate 3D bounding boxes and save the results (CSV and visualization) to the `output/` directory.
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## Citation
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If you find Boxer useful in your research, please consider citing:
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```bibtex
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@article{boxer2026,
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title={Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D},
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author={Daniel DeTone and Tianwei Shen and Fan Zhang and Lingni Ma and Julian Straub and Richard Newcombe and Jakob Engel},
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year={2026},
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}
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```
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