BirdNet+ software implements a state-of-the-art 3D object detection algorithm based only on LiDAR technology. It represents a clear advance on its predecessor, the BirdNet. Algorithm developed at the Universidad Carlos III de Madrid.

• The framework behind the algorithm is Detectron2 in Pytorch.
• Removes all the post processing stage using only the network to perform 3D predictions, which improves the detection accuracy.

You will need the Detectron2 requirements:

• Linux with Python ≥ 3.6
• PyTorch ≥ 1.3
• torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
• OpenCV
• pycocotools: pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

After having the above dependencies and gcc & g++ ≥ 5, run:

git clone https://github.com/AlejandroBarrera/birdnet2
cd birdnet2 && python -m pip install -e .

Please refer to the Detectron2 installation section for more details.

1. Add birdnet2 top level to your PYTHONPATH.

export DETECTRON_ROOT=/path/to/birdnet2
export PYTHONPATH=$PYTHONPATH:$DETECTRON_ROOT

2. Download the pre-trained model from here and put it in a new folder named models inside DETECTRON_ROOT (you can change this path through the field OUTPUT_DIR in the configuration file Base-BirdNetPlus.yaml)
3. Launch the script python demo/demo_BirdNetPlus.py for an example of how BirdNet+ works.

This demo uses data from the KITTI Vision Benchmark Suite.

0. Follow the steps 1 and 2 from Quick Start.
1. Download the training and validation splits here.
2. To train with KITTI object detection dataset:

• Download the dataset.
• Generate BEV KITTI images. The tool that we used can be found at lidar_bev.
• In DETECTRON_ROOT, arrange everything according to the directory tree below (We leave annotations folder empty for now):

.
|-- datasets
|   |-- bv_kitti
|   |   |-- annotations
|   |   |   |-- {train,val} JSON files
|   |   |-- lists
|   |   |   |-- {train,val} splits
|   |   |-- image
|   |   |   |-- BEV KITTI {train,val} images
|   |   |-- label
|   |   |   |-- KITTI {train,val} labels
|   |   |-- calib
|   |   |   |-- KITTI calibration files

NOTE: In the current version, the label subfolder must contain the original KITTI annotations (label_2).

3. Launch python tools/train_net_BirdNetPlus.py --config_file Base-BirdNetPlus with the parameters required inside of the configuration file. The annotations are generated automatically.
4. For validation use python tools/val_net_BirdNetPlus.py instead with as many arguments as you want. Please review the arguments carefully. For the evaluation, we strongly recommend using an offline KITTI evaluator such as eval_kitti after obtaining the evaluation annotations.

If you use BirdNet+ in your research, please cite our work using the following BibTeX entry.

@article{barrera2021birdnet+,
  title={BirdNet+: Two-Stage 3D Object Detection in LiDAR Through a Sparsity-Invariant Bird’s Eye View},
  author={Barrera, Alejandro and Beltr{\'a}n, Jorge and Guindel, Carlos and Iglesias, Jos{\'e} Antonio and Garc{\'\i}a, Fernando},
  journal={IEEE Access},
  volume={9},
  pages={160299--160316},
  year={2021},
  publisher={IEEE}
}