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🥊 Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3D

🎉 Boxer has been accepted to ECCV 2026!

Boxer lifts 2D object detections into static, global, fused 3D oriented bounding boxes (OBBs) from posed images and semi-dense point clouds, focused on indoor object detection. This repo contains the code and pre-trained model (no training code) needed to run Boxer on a variety of input data sources (inference only code).

Project Page | ArXiv | Video | HF-Model | HF-Data | GitHub Code

Installation

We tested on MacOS (with mps acceleration) and Fedora (with CUDA acceleration).

# Install uv (https://docs.astral.sh/uv/)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment with uv
uv venv boxer --python 3.12
source boxer/bin/activate

# Core dependencies for running Boxer
uv pip install 'torch>=2.0' numpy opencv-python tqdm dill

# To support Project Aria loading
uv pip install projectaria-tools

# 3D interactive viewer for view_*.py scripts
uv pip install moderngl moderngl-window imgui-bundle

Download Model Checkpoints

We host model checkpoints for BoxerNet, DinoV3 and OWLv2 on HuggingFace. Download them to the ckpts/ directory:

bash scripts/download_ckpts.sh

Download Sample Project Aria Data

In this repo, we provide sample code for running on the following data sources:

• Project Aria Gen 1 & 2
• CA-1M
• SUN-RGBD
• ScanNet (manual download needed)

Let's first start with Aria data. We host three sample Project Aria sequences (hohen_gen1, nym10_gen1, cook0_gen2) on HuggingFace. Download them to the sample_data/ directory:

bash scripts/download_aria_data.sh

Demo #1: Hello World / Run BoxerNet in headless mode

For this first demo, you do not need to have a display, so it will work if you are SSH'ed into a server. This will run BoxerNet on the first 90 images of a sequence from the test set of the NymeriaPlus dataset. This will confirm we can load up the data and run a forward passes with the model alongside the online tracker.

Expected to take ~2 mins on mac MPS, <15 secs on CUDA.

python run_boxer.py --input nym10_gen1 --max_n=90 --track

This will dump out static images and a video to outputs/nym10_gen1/, e.g. something like this in outputs/nym10_gen1/boxer_viz_current.png

Demo #2: BoxerNet Interactive Demo on Aria Data

For this demo, you need to have a valid display to have the GUI work. This demo allows you to create 2DBB prompts and enter text to prompt OWL to detect objects. Run it like:

python view_prompt.py --input nym10_gen1

You should see a window that looks like this:

You can also run it on the other Project Aria sequences:

• python view_prompt.py --input hohen_gen1
• python view_prompt.py --input cook0_gen2

Demo #3: Visualize Offline Fusion

Make sure to run Demo #1 first. This generates 2DBB and 3DBB csv files, for example:

• output/nym10_gen1/boxer_3dbbs.csv
• output/nym10_gen1/owl_2dbbs.csv

Then, run the fusion script, which will by default search the above paths, to load and fuse the 3DBBs from above.

python view_fusion.py --input nym10_gen1

You should see a window like this:

Demo #4: Online Tracker (requires Demo #1)

Make sure to run Demo #1 above first to generate the 2DBB and 3DBB CSVs. Run the online tracker, which will estimate 3DBBs on the fly as new images are observed:

python view_tracker.py --input nym10_gen1 --autoplay

Demo #5: Running on CA-1M data

Extract a sample validation sequence (ca1m-val-42898570) to sample_data/

python scripts/download_ca1m_sample.py

Run the view_prompt.py script on it:

python view_prompt.py --input ca1m-val-42898570

You should see a window like this:

Demo #6: Running on SUN-RGBD data

Download a subset of Omni3D SUN-RGBD: extract 20 sample images to sample_data/

python scripts/download_omni3d_sample.py

Run the view_prompt.py script on it:

python view_prompt.py --input SUNRGBD

You should see a window like this:

Demo #7: Running on ScanNet data

ScanNet must be manually downloaded from https://github.com/scannet/scannet. Once you do that, place the scene directory in sample_data/, e.g. sample_data/scene0707_00

Run just like the above examples:

python view_prompt.py --input scene0707_00

run_boxer.py Usage Details

The pipeline supports optional online 3D tracking (--track) for temporal consistency and offline 3D fusion (--fuse) for merging detections across frames after all detections have been made.

# Run on a sample Aria sequence
python run_boxer.py --input hohen_gen1

# Disable visualization (faster, just writes CSV)
python run_boxer.py --input hohen_gen1 --skip_viz

# Custom text prompts
python run_boxer.py --input hohen_gen1 --labels=chair,table,lamp

# Run with online 3D tracking
python run_boxer.py --input hohen_gen1 --track

# Run with post-hoc 3D box fusion
python run_boxer.py --input hohen_gen1 --fuse

# ScanNet sequence
python run_boxer.py --input scene0084_02

# CA-1M sequence
python run_boxer.py --input ca1m-val-42898570

# Omni3D dataset
python run_boxer.py --input SUNRGBD

# Adjust thresholds
python run_boxer.py --input hohen_gen1 --thresh2d 0.3 --thresh3d 0.6

# Force a specific precision (auto-detects bfloat16 on supported CUDA GPUs)
python run_boxer.py --input hohen_gen1 --force_precision float32

Outputs

Results are written to output/<sequence_name>/:

• boxer_3dbbs.csv — per-frame 3D bounding boxes
• owl_2dbbs.csv — per-frame 2D detections
• boxer_3dbbs_tracked.csv — tracked 3D boxes (with --track)
• boxer_viz_final.mp4 — visualization video

CLI Reference

Flag	Default	Description
--input		Path to input sequence
--detector	owl	2D detector (owl)
--labels	lvisplus	Comma-separated text prompts, or a taxonomy name
--thresh2d	0.2	2D detection confidence threshold
--thresh3d	0.5	3D box confidence threshold
--track	off	Enable online 3D box tracking
--fuse	off	Run post-hoc 3D box fusion
--skip_viz	off	Disable visualization (on by default)
--force_precision	auto	Override inference precision (float32 or bfloat16). Auto-detects bfloat16 on supported CUDA GPUs
--camera	rgb	Aria camera stream (rgb, slaml, slamr)
--pinhole	off	Rectify fisheye to pinhole
--detector_hw	960	Resize for 2D detector
--ckpt	see code	Path to BoxerNet checkpoint
--output_dir	output/	Output directory
--gt2d	off	Use ground-truth 2D boxes as input
--no_sdp	off	Disable semi-dense point input
--force_cpu	off	Force CPU inference

Project Structure

boxer/
├── run_boxer.py              # Main entry point (headless detection + lifting)
├── view_prompt.py            # Interactive demo (2D prompts + OWL text detection)
├── view_fusion.py            # View pre-computed 3D bounding boxes
├── boxernet/
│   ├── boxernet.py           # BoxerNet model (encode → cross-attend → predict)
│   └── dinov3_wrapper.py     # DINOv3 backbone wrapper
├── owl/
│   ├── owl_wrapper.py        # OWLv2 open-vocabulary detector
│   └── clip_tokenizer.py     # CLIP BPE tokenizer + text embedder
├── loaders/
│   ├── base_loader.py        # Base loader interface
│   ├── aria_loader.py        # Project Aria data loader
│   ├── ca_loader.py          # CA-1M dataset loader
│   ├── omni_loader.py        # Omni3D dataset loader
│   └── scannet_loader.py     # ScanNet dataset loader
├── scripts/
│   ├── download_ckpts.sh     # Download model checkpoints
│   ├── download_aria_data.sh # Download sample Aria sequences
│   ├── download_ca1m_sample.py      # Extract CA-1M sample data
│   ├── download_omni3d_sample.py    # Extract Omni3D SUN-RGBD sample
├── tests/                    # Unit tests (see tests/README.md)
└── utils/
    ├── viewer_3d.py          # Interactive 3D visualization + viewer classes
    ├── tw/                   # TensorWrapper types (see utils/tw/README.md)
    │   ├── tensor_wrapper.py # TensorWrapper base class
    │   ├── camera.py         # CameraTW: camera intrinsics + projection
    │   ├── obb.py            # ObbTW tensor wrapper + IoU computation
    │   └── pose.py           # PoseTW: SE(3) poses + quaternion math
    ├── fuse_3d_boxes.py      # 3D box fusion + Hungarian algorithm
    ├── track_3d_boxes.py     # Online 3D bounding box tracker
    ├── file_io.py            # CSV I/O for OBBs and calibration
    ├── image.py              # Image utilities + 3D/2D box rendering
    ├── gravity.py            # Gravity alignment utilities
    ├── taxonomy.py           # Label taxonomy definitions
    ├── demo_utils.py         # Demo helpers, paths, timing
    └── video.py              # Video I/O utilities

Adding Additional Datasets

For the minimal single image lifting with BoxerNet, we require:

• image
• intrinsics calibration (we tested with both Pinhole and Fisheye624 camera models)
• the 3D gravity direction
• Depth is optional but improves performance significantly

For lifting a video sequence we need the same as above plus:

• full 6 DoF pose for each image

FAQ

Q: Can I run it on an arbitrary image without any other info? A: Theoretically yes, but you would need to estimate the intrinsics and gravity direction. We didn't test that.

Q: Do you plan to release the training or evaluation code? A: No, we do not, because that would require more long-term maintenance from the authors. You can email the first author or leave a GitHub issue if you have any questions about re-implementing the training/evaluation pipeline, but our response may be slow.

Q: Does it work on a Windows machine? A: We did not test it, but running the core model should work.

Linting

We use ruff for linting and formatting:

uv pip install ruff

# Check for lint errors
ruff check .

# Auto-fix lint errors
ruff check --fix .

# Format code
ruff format .

Testing

uv pip install pytest pytest-cov

# Run all tests
bash tests/run_tests.sh

# Run a single test file
bash tests/run_tests.sh test_gravity

# Run without opening the coverage report
bash tests/run_tests.sh --no-open

Citation

If you find Boxer useful in your research, please consider citing:

@inproceedings{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},
      booktitle={European Conference on Computer Vision (ECCV)},
      year={2026},
}

License

The majority of Boxer is licensed under CC-BY-NC. See the LICENSE file for details. However portions of the project are available under separate license terms: see NOTICE.