metadata
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 | Paper | GitHub
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:
# 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):
python run_boxer.py --input nym10_gen1 --max_n=90 --track
Citation
@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},
}