# BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

> [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://arxiv.org/abs/2205.13542)

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## Abstract

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of such methods, especially for semantic-oriented tasks (such as 3D scene segmentation). In this paper, we break this deeply-rooted convention with BEVFusion, an efficient and generic multi-task multi-sensor fusion framework. It unifies multi-modal features in the shared bird's-eye view (BEV) representation space, which nicely preserves both geometric and semantic information. To achieve this, we diagnose and lift key efficiency bottlenecks in the view transformation with optimized BEV pooling, reducing latency by more than 40x. BEVFusion is fundamentally task-agnostic and seamlessly supports different 3D perception tasks with almost no architectural changes. It establishes the new state of the art on nuScenes, achieving 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation, with 1.9x lower computation cost. Code to reproduce our
results is available at https://github.com/mit-han-lab/bevfusion.

<div align=center>
<img src="https://user-images.githubusercontent.com/34888372/215313913-4b43f8a1-e2e2-49ba-b631-992155351922.png" width="800"/>
</div>

## Introduction

We implement BEVFusion and provide the results and pretrained checkpoints on NuScenes dataset.

## Usage

<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->

### Compiling operations on CUDA

**Note** that the voxelization OP in the original implementation of `BEVFusion` is different from the implementation in MMCV. If you want to use the original pretrained model [here](https://github.com/mit-han-lab/bevfusion/blob/main/README.md), you need to use the original implementation of voxelization OP.

```python
python projects/BEVFusion/setup.py develop
```

### Training commands

In MMDetection3D's root directory, run the following command to train the model:

```bash
python tools/train.py projects/BEVFusion/configs/bevfusion_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py
```

For multi-gpu training, run:

```bash
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=${NUM_GPUS} --master_port=29506 --master_addr="127.0.0.1" tools/train.py projects/BEVFusion/configs/bevfusion_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py
```

### Testing commands

In MMDetection3D's root directory, run the following command to test the model:

```bash
python tools/train.py projects/BEVFusion/configs/bevfusion_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py ${CHECKPOINT_PATH}
```

## Results and models

### NuScenes

|                                    Backbone                                     | Voxel type (voxel size) | NMS | Mem (GB) | Inf time (fps) |  NDS  |  mAP  |                                                 Download                                                 |
| :-----------------------------------------------------------------------------: | :---------------------: | :-: | :------: | :------------: | :---: | :---: | :------------------------------------------------------------------------------------------------------: |
| [SECFPN](./configs/bevfusion_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py) |      voxel (0.075)      |  ×  |    -     |       -        | 71.62 | 68.77 | [converted_model](https://drive.google.com/file/d/1QkvbYDk4G2d6SZoeJqish13qSyXA4lp3/view?usp=share_link) |

## Citation

```latex
@inproceedings{liu2022bevfusion,
  title={BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation},
  author={Liu, Zhijian and Tang, Haotian and Amini, Alexander and Yang, Xingyu and Mao, Huizi and Rus, Daniela and Han, Song},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2023}
}
```

## Checklist

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