bevfusion 学习笔记

目录

[tensorrt ros部署:](#tensorrt ros部署:)

[也依赖ros2 c++](#也依赖ros2 c++)

ros2安装指导:

相机标定工具源码:

官方github,部分模型开源


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:

GitHub - linClubs/Calibration-Is-All-You-Need: calibration is you need including camera、imu、camera2camera、 camera2lidar、imu2camera、imu2lidar.

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

GitHub - mit-han-lab/bevfusion: [ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

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
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