BroadBEV

This repository contains the official implementation of BroadBEV, 24' ICRA: https://arxiv.org/abs/2309.01409

Environment Setup

1. Use the below settings. To configure the other packages, refer to environment.yaml.

• Python >= 3.8, <3.9
• OpenMPI = 4.0.4 and mpi4py = 3.0.3 (Needed for torchpack)
• Pillow = 8.4.0
• PyTorch >= 1.9, <= 1.10.2
• tqdm
• torchpack
• mmcv = 1.4.0
• mmdetection = 2.20.0
• nuscenes-dev-kit

2. Run the below command after installing the python packages.

python setup.py develop

Data Preparation

For Setup details, follow the guidelines of here.
BroadBEV project folder should contain the below files.

BroadBEV
├── mmdet3d
├── pretrained
├── configs
├── tools
├── data
│   ├── nuscenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
│   │   ├── v1.0-trainval
│   │   ├── nuscenes_database
│   │   ├── nuscenes_infos_train.pkl
│   │   ├── nuscenes_infos_val.pkl 
│   │   ├── nuscenes_infos_test.pkl
│   │   ├── nuscenes_dbinfos_train.pkl

Pretrained Models

You can download below models on this link.

Train & Evaluation

Note that we use 4 NVIDIA A100 80GB in our experiments.
Any other configurations does not guarantee stable training.

bash scripts/train.sh
bash scripts/eval.sh

Acknowlegment

This work is mainly based on MIT BEVFusion, we thank the authors for their contribution.