Add dataset card, link to paper and code

README.md

@@ -1,3 +1,52 @@
----
-license: cc-by-nc-4.0
----
+---
+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},
+}
+```