目录
[tensorrt ros部署：](#tensorrt ros部署：)
[也依赖ros2 c++](#也依赖ros2 c++)
tensorrt ros部署：
https://github.com/linClubs/BEVFusion-ROS-TensorRT
也依赖ros2 c++
GitHub - newintelligence4/BEVfusion_preprocess: Multiple Lidar preprocessor for BEVfusion
ros2安装指导：
ROS2学习笔记（一）------Win11安装及使用 - 知乎
安装手册：
Windows (binary) --- ROS 2 Documentation: Humble documentation
下载地址：Releases · ros2/ros2 · GitHub
相机标定工具源码：
官方github，部分模型开源
https://github.com/ADLab-AutoDrive/BEVFusion
没有开源centerpoint版，
Main Results
nuScenes detection test
| Model | Head | 3DBackbone | 2DBackbone | mAP | NDS | Link |
|---|---|---|---|---|---|---|
| BEVFusion | TransFusion-L | VoxelNet | Dual-Swin-T | 69.2 | 71.8 | Detection |
| BEVFusion* | TransFusion-L | VoxelNet | Dual-Swin-T | 71.3 | 73.3 | Leadboard |
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 | - |
| BEVFusion* | TransFusion-L | VoxelNet | Dual-Swin-T | 69.6 | 72.1 | Model |
3D Object Detection (on nuScenes validation)
| Model | Modality | mAP | NDS | Checkpoint |
|---|---|---|---|---|
| BEVFusion | C+L | 68.52 | 71.38 | Link |
| Camera-Only Baseline | C | 35.56 | 41.21 | Link |
| LiDAR-Only Baseline | L | 64.68 | 69.28 | Link |
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 operator, this model runs fast and has higher mAP than BEVDet-Tiny under the same input resolution. Please refer to BEVDet repo for the original BEVDet-Tiny implementation. The LiDAR-only baseline is TransFusion-L.
BEV Map Segmentation (on nuScenes validation)
| Model | Modality | mIoU | Checkpoint |
|---|---|---|---|
| BEVFusion | C+L | 62.95 | Link |
| Camera-Only Baseline | C | 57.09 | Link |
| LiDAR-Only Baseline | L | 48.56 | Link |