| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - object-detection |
| --- |
| |
| # Boxer Dataset |
|
|
| This repository contains sample data for **Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D**. |
|
|
| [Project Page](https://facebookresearch.github.io/boxer) | [Paper](https://huggingface.co/papers/2604.05212) | [GitHub](https://github.com/facebookresearch/boxer) |
|
|
| ## Introduction |
| Boxer is an algorithm designed to estimate static 3D bounding boxes (3DBBs) from 2D open-vocabulary object detections, posed images, and optional depth. This repository hosts sample data sequences used to demonstrate Boxer's ability to lift 2D detections into 3D world space. |
|
|
| ## Data Sources |
| The dataset provides sample sequences from several sources used in the paper: |
| * **Project Aria**: Sequences from Gen 1 & 2 (e.g., `hohen_gen1`, `nym10_gen1`, `cook0_gen2`). |
| * **CA-1M**: A sample validation sequence. |
| * **SUN-RGBD**: A subset of sample images. |
|
|
| ## Usage |
|
|
| ### Download Data |
| To download the sample data to your local machine, you can use the utility scripts provided in the [official GitHub repository](https://github.com/facebookresearch/boxer): |
|
|
| ```bash |
| # Download sample Aria sequences |
| bash scripts/download_aria_data.sh |
| |
| # Download CA-1M sample |
| python scripts/download_ca1m_sample.py |
| |
| # Download SUN-RGBD sample |
| python scripts/download_omni3d_sample.py |
| ``` |
|
|
| ### Run Inference |
| Once the data and checkpoints are downloaded, you can run BoxerNet on a sequence (e.g., `nym10_gen1`): |
|
|
| ```bash |
| python run_boxer.py --input nym10_gen1 --max_n=90 --track |
| ``` |
|
|
| ## Citation |
| ```bibtex |
| @article{boxer2026, |
| title={Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D}, |
| author={Daniel DeTone and Tianwei Shen and Fan Zhang and Lingni Ma and Julian Straub and Richard Newcombe and Jakob Engel}, |
| year={2026}, |
| } |
| ``` |
