2024/06/28 - RCBEVDet++ achieves SOTA 3D object detection, BEV semantic segmentation, and 3D multi-object tracking results on nuScenes benchmark. The paper and code for RCBEVDet++ is coming soon~
2024/06/01 - Code for RCBEVDet is released.
Weight & Code
Model weights for RCBEVDet are released: google drive
Code: please sign the application to obtain the code
Results
3D Object Detection (nuScenes Validation)
Method
Input
Backbone
NDS
mAP
BEVDepth4D
C
ResNet-50
51.9
40.5
RCBEVDet
C+R
ResNet-50
56.8
45.3
SparseBEV
C
ResNet-50
54.5
43.2
RCBEVDet++
C+R
ResNet-50
60.4
51.9
3D Object Detection (nuScenes Test)
Method
Input
Backbone
Future frame
NDS
mAP
BEVDepth4D
C
V2-99
No
60.5
51.5
RCBEVDet
C+R
V2-99
No
63.9
55.0
SparseBEV
C
V2-99
No
63.6
55.6
RCBEVDet++
C+R
V2-99
No
68.7
62.6
SparseBEV
C
ViT-L
Yes
70.2
——
RCBEVDet++
C+R
ViT-L
Yes
72.7
67.3
BEV Semantic Segmentation (nuScenes Validation)
Method
Input
Backbone
mIoU
RCBEVDet++
C+R
ResNet-101
62.8
3D Multi-object Tracking (nuScenes Test)
Method
Input
Backbone
AMOTA
AMOTP
RCBEVDet++
C+R
ViT-L
59.6
0.713
Acknowledgements
The overall code are based on mmdetection3D, BEVDet and SparseBEV. We sincerely thank the authors for their great work.
License
The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact wyt@pku.edu.cn.
About
[CVPR 2024] RCBEVDet: Radar-camera Fusion in Bird’s Eye View for 3D Object Detection