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**If you are interested in getting updates, please sign up [here](https://docs.google.com/forms/d/e/1FAIpQLSfkmfsX45HstL5rUQlS7xJthhS3Z_Pm2NOVstlXUqgaK4DEfQ/viewform) to get notified!** **If you are interested in getting updates, please sign up [here](https://docs.google.com/forms/d/e/1FAIpQLSfkmfsX45HstL5rUQlS7xJthhS3Z_Pm2NOVstlXUqgaK4DEfQ/viewform) to get notified!**
- **(2022/6/3)** BEVFusion ranks first on [nuScenes](https://nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) among all solutions.
- **(2022/6/3)** We released the first version of BEVFusion (with pre-trained checkpoints and evaluation). - **(2022/6/3)** We released the first version of BEVFusion (with pre-trained checkpoints and evaluation).
- **(2022/5/26)** BEVFusion is released on arXiv. - **(2022/5/26)** BEVFusion is released on [arXiv](https://arxiv.org/abs/2205.13542).
- **(2022/5/2)** BEVFusion ranks first on [nuScenes](https://nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) among all solutions that do not use test-time augmentation and model ensemble.
## Abstract ## Abstract
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## Results ## Results
### 3D Object Detection on nuScenes test ### 3D Object Detection (on nuScenes test)
| Model | Modality | mAP | NDS | | Model | Modality | mAP | NDS |
| :-------: | :------: | :--: | :--: | | :-------: | :------: | :--: | :--: |
| [BEVFusion](configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/convfuser.yaml) | C+L | 70.23 | 72.88 | | BEVFusion-e | C+L | 74.99 | 76.09 |
| BEVFusion | C+L | 70.23 | 72.88 |
### 3D Object Detection on nuScenes validation ### 3D Object Detection (on nuScenes validation)
| Model | Modality | mAP | NDS | Checkpoint | | Model | Modality | mAP | NDS | Checkpoint |
| :------------------: | :------: | :--: | :--: | :---------: | | :------------------: | :------: | :--: | :--: | :---------: |
@ -33,7 +36,7 @@ Multi-sensor fusion is essential for an accurate and reliable autonomous driving
*Note*: The camera-only object detection baseline is a variant of BEVDet-Tiny with a much heavier view transformer and other differences in hyperparameters. Thanks to our [efficient BEV pooling](mmdet3d/ops/bev_pool) operator, this model runs fast and has higher mAP than BEVDet-Tiny under the same input resolution. Please refer to [BEVDet repo](https://github.com/HuangJunjie2017/BEVDet) for the original BEVDet-Tiny implementation. The LiDAR-only baseline is TransFusion-L. *Note*: The camera-only object detection baseline is a variant of BEVDet-Tiny with a much heavier view transformer and other differences in hyperparameters. Thanks to our [efficient BEV pooling](mmdet3d/ops/bev_pool) operator, this model runs fast and has higher mAP than BEVDet-Tiny under the same input resolution. Please refer to [BEVDet repo](https://github.com/HuangJunjie2017/BEVDet) for the original BEVDet-Tiny implementation. The LiDAR-only baseline is TransFusion-L.
### BEV Map Segmentation on nuScenes validation ### BEV Map Segmentation (on nuScenes validation)
| Model | Modality | mIoU | Checkpoint | | Model | Modality | mIoU | Checkpoint |
| :------------------: | :------: | :--: | :---------: | | :------------------: | :------: | :--: | :---------: |