End-to-End-Multi-View-Fusion-for-3D-Object-Detection-in-LiDAR-Point-Clouds
End-to-End-Multi-View-Fusion-for-3D-Object-Detection-in-LiDAR-Point-Clouds copied to clipboard
Published
20 hours ago •
AndyYuan96
AndyYuan96
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
This is an unofficial implementation of MVF based on PCDet and PointCloudDynamicVoxel.
How to use
Follow PCDet and PointCloudDynamicVoxel install guide.
Performance
DV+SV (same setting with PCDet's pointpillar config except dynamic voxel)
config : tools/mvf/mvf_pp_dv.yaml
model : output/mvf_pp_dv.pth
INFO Car [email protected], 0.70, 0.70:
bbox AP:94.2958, 89.5560, 88.6962
bev AP:89.6566, 86.9236, 84.3930
3d AP:87.4800, 77.1988, 75.5659
aos AP:94.27, 89.31, 88.33
MVF ([lr = 0.0015, epoch 100, only car](paper setting), batch 6)
config : tools/mvf/mvf_paper_car.yaml
model : output/mvf_paper_car.pth
INFO Car [email protected], 0.70, 0.70:
bbox AP:90.7383, 89.9176, 88.7714
bev AP:90.0564, 87.7798, 85.5016
3d AP:88.0401, 78.4811, 76.5275
aos AP:90.72, 89.77, 88.52
NOTE
As paper don't say too much about voxelization in perspective view, I just implementation according to my understanding, and the result is a little lower than paper. For the resolution of perspective view, too small(16,64) or too large(64, 512) don't get good result, (16, 128) is relatively good.
AndyYuan96