Code of BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework.

This paper focuses on LiDAR-camera fusion for 3D object detection. If you find this project useful, please cite:

  title={{BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework}},
  author={Tingting Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang, Bing Wang and Zhi Tang},


Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discover that this underlying assumption makes the current fusion framework infeasible to produce any prediction when there is a LiDAR malfunction, regardless of minor or major. This fundamentally limits the deployment capability to realistic autonomous driving scenarios. In contrast, we propose a surprisingly simple yet novel fusion framework, dubbed BEVFusion, whose camera stream does not depend on the input of LiDAR data, thus addressing the downside of previous methods. We empirically show that our framework surpasses the state-of-the-art methods under the normal training settings. Under the robustness training settings that simulate various LiDAR malfunctions, our framework significantly surpasses the state-of-the-art methods by 15.7% to 28.9% mAP. To the best of our knowledge, we are the first to handle realistic LiDAR malfunction and can be deployed to realistic scenarios without any post-processing procedure.


Main Results

nuScenes detection test

Model Head 3DBackbone 2DBackbone mAP NDS Link
BEVFusion TransFusion-L VoxelNet Dual-Swin-T 69.2 71.8 Detection

nuScenes detection validation

Model Head 3DBackbone 2DBackbone mAP NDS Model
BEVFusion PointPillars Dual-Swin-T 22.9 31.1 Model
BEVFusion PointPillars PointPillars 35.1 49.8 Model
BEVFusion PointPillars PointPillars Dual-Swin-T 53.5 60.4 Model
BEVFusion CenterPoint Dual-Swin-T 27.1 32.1
BEVFusion CenterPoint VoxelNet 57.1 65.4
BEVFusion CenterPoint VoxelNet Dual-Swin-T 64.2 68.0
BEVFusion TransFusion-L Dual-Swin-T 22.7 26.1
BEVFusion TransFusion-L VoxelNet 64.9 69.9
BEVFusion TransFusion-L VoxelNet Dual-Swin-T 67.9 71.0

nuScenes detection validation against LiDAR Malfunctions

LiDAR Malfunctions Visualization

Infos for random box dropping in validation set are in drop_foreground.json, with LiDAR file name: dropping box (True) or not (False).

Model Limited FOV Objects failure Head 3DBackbone 2DBackbone mAP NDS Model
BEVFusion (-π/3,π/3) False PointPillars PointPillars Dual-Swin-T 33.5 42.1 Model
BEVFusion (-π/2,π/2) False PointPillars PointPillars Dual-Swin-T 36.8 45.8 Model
BEVFusion True PointPillars PointPillars Dual-Swin-T 41.6 51.9 Model
BEVFusion (-π/3,π/3) False CenterPoint VoxelNet Dual-Swin-T 40.9 49.9
BEVFusion (-π/2,π/2) False CenterPoint VoxelNet Dual-Swin-T 45.5 54.9
BEVFusion True CenterPoint VoxelNet Dual-Swin-T 54.0 61.6
BEVFusion (-π/3,π/3) False TransFusion-L VoxelNet Dual-Swin-T 41.5 50.8
BEVFusion (-π/2,π/2) False TransFusion-L VoxelNet Dual-Swin-T 46.4 55.8
BEVFusion True TransFusion-L VoxelNet Dual-Swin-T 50.3 57.6

Use BEVFusion


Please refer to for installation of mmdet3d.

Recommended environments:

mmdet==2.11.0 (please install mmdet in mmdetection-2.11.0)

Benchmark Evaluation and Training

Please refer to to prepare the data. Then follow the instruction there to train our model. All detection configurations are included in configs.

# training example for bevfusion-pointpillar 
# train nuimage for camera stream backbone and neck.
./tools/ configs/bevfusion/cam_stream/ 8
# first train camera stream
./tools/ configs/bevfusion/cam_stream/ 8
# then train LiDAR stream
./tools/ configs/bevfusion/lidar_stream/ 8
# then train BEVFusion
./tools/ configs/bevfusion/ 8

### evaluation example for bevfusion-pointpillar
./tools/ configs/bevfusion/ ./work_dirs/bevfusion_pp.pth 8 --eval bbox


We sincerely thank the authors of mmdetection3d, TransFusion, LSS, CenterPoint for open sourcing their methods.


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