Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
Anonymous
97.19 %
98.27 %
94.59 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
2
GraR-VoI
96.38 %
96.81 %
91.20 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
3
GraR-Po
96.18 %
96.84 %
91.11 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
4
SFD
code
96.17 %
98.97 %
91.13 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D
Detection with Depth Completion . CVPR 2022.
5
VPFNet
96.15 %
96.64 %
91.14 %
0.06 s
2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection
with Virtual Point based LiDAR and Stereo Data
Fusion . 2021.
6
Anonymous
96.12 %
99.07 %
91.12 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
7
CLOCs
code
96.07 %
96.77 %
91.11 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection . 2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
8
GraR-Vo
96.05 %
96.67 %
93.01 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
9
TED
96.03 %
96.64 %
93.35 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
10
ImpDet
96.00 %
96.73 %
90.96 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
11
CLOCs_PVCas
code
95.96 %
96.76 %
91.08 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection . 2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
12
CityBrainLab
95.96 %
96.59 %
90.94 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
13
PE-RCVN
95.94 %
96.90 %
90.98 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
14
SPT
95.92 %
96.57 %
91.04 %
0.1 s
GPU @ 2.5 Ghz (Python)
15
Anonymous
95.91 %
96.55 %
92.86 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
16
PVT-SSD
95.90 %
96.75 %
90.69 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
17
GraR-Pi
95.89 %
98.59 %
92.85 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
18
GLENet-VR
95.81 %
96.85 %
90.91 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
19
HCPVF
95.80 %
96.62 %
93.03 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
20
Anonymous
95.79 %
96.69 %
92.75 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
21
SECOND
95.79 %
96.44 %
90.55 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
22
DVF-V
95.77 %
96.60 %
90.89 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object
Detection . 2022.
23
LIVOX_Det
95.75 %
98.62 %
93.05 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
24
Fast-CLOCs
95.75 %
96.69 %
90.95 %
0.1 s
GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR
Object Candidates Fusion for 3D Object Detection . Proceedings of the IEEE/CVF
Winter Conference on Applications of Computer
Vision (WACV) 2022.
25
DSGN++
code
95.70 %
98.08 %
88.27 %
0.2 s
GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation
for Stereo-based 3D Detectors . arXiv preprint arXiv:2204.03039 2022.
26
TBD
95.69 %
96.33 %
93.07 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
27
SGFusion
95.67 %
96.57 %
92.69 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
28
CasA
95.62 %
96.52 %
92.86 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
29
BADet
code
95.61 %
98.75 %
90.64 %
0.14 s
1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object
Detection
from Point Clouds . Pattern Recognition 2022.
30
SE-SSD
code
95.60 %
96.69 %
90.53 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object
Detector From Point Cloud . CVPR 2021.
31
JPVNet
95.52 %
96.41 %
90.72 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
32
DVF-PV
95.49 %
96.42 %
92.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object
Detection . 2022.
33
Anonymous
95.47 %
98.35 %
90.55 %
n/a s
1 core @ 2.5 Ghz (C/C++)
34
SPANet
95.46 %
96.54 %
90.47 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network
for 3D Object Detection . Pacific Rim International Conference on Artificial
Intelligence 2021.
35
ISE-RCNN-PV
95.46 %
96.20 %
92.88 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
36
LGNet
95.43 %
96.52 %
92.73 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
37
TBD
95.43 %
96.06 %
90.38 %
0.1 s
1 core @ 2.5 Ghz (Python)
38
ISE-RCNN
95.43 %
96.38 %
92.82 %
0.09 s
1 core @ 2.5 Ghz (Python + C/C++)
39
Anonymous
95.41 %
96.49 %
90.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
40
GLENet
95.40 %
96.61 %
90.57 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
41
Anonymous
95.40 %
96.62 %
90.56 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
42
PTA-RCNN
95.39 %
96.40 %
92.40 %
0.08 s
1 core @ 2.5 Ghz (Python)
43
SASA
code
95.35 %
96.01 %
92.53 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction
for Point-based 3D Object Detection . arXiv preprint arXiv:2201.01976 2022.
44
SPG_mini
code
95.32 %
96.23 %
92.68 %
0.09 s
GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for
3D Object Detection via Semantic Point
Generation . Proceedings of the IEEE conference on
computer vision and pattern recognition (ICCV) 2021.
45
EQ-PVRCNN
code
95.32 %
98.23 %
92.65 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud
Understanding . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 2022.
46
anonymous
95.31 %
96.36 %
92.57 %
0.09 s
GPU @ 2.5 Ghz (Python)
47
Focals Conv
code
95.28 %
96.30 %
92.69 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object
Detection . Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition 2022.
48
CasA++
95.28 %
95.83 %
94.28 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
49
DGDNH
95.24 %
98.36 %
92.69 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
50
VoxSeT
code
95.23 %
96.16 %
90.49 %
33 ms
1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach
to 3D Object Detection from Point Clouds . CVPR 2022.
51
PC-CNN-V2
95.20 %
96.06 %
89.37 %
0.5 s
GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles . 2018 IEEE International Conference on Robotics
and Automation (ICRA) 2018.
52
VPFNet
code
95.17 %
96.06 %
92.66 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network
for Multi-class 3D Object Detection . 2021.
53
F-PointNet
code
95.17 %
95.85 %
85.42 %
0.17 s
GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data . arXiv preprint arXiv:1711.08488 2017.
54
EPNet++
95.17 %
96.73 %
92.10 %
0.1 s
GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for
Multi-Modal 3D Object Detection . arXiv preprint arXiv:2112.11088 2021.
55
SA-SSD
code
95.16 %
97.92 %
90.15 %
0.04 s
1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud . CVPR 2020.
56
HMFI
code
95.16 %
96.29 %
92.45 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
57
Pyramid R-CNN
95.13 %
95.88 %
92.62 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and
Adaptability for 3D Object Detection . ICCV 2021.
58
Voxel R-CNN
code
95.11 %
96.49 %
92.45 %
0.04 s
GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance
Voxel-based 3D Object Detection
. AAAI 2021.
59
3DSSD
code
95.10 %
97.69 %
92.18 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object
Detector . CVPR 2020.
60
GV-RCNN
code
95.06 %
96.29 %
92.43 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
61
PV-RCNN++
code
95.05 %
96.08 %
92.42 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
62
PDV
code
95.00 %
96.07 %
92.44 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection . CVPR 2022.
63
ATT_SSD
94.99 %
96.02 %
92.18 %
0.01 s
1 core @ 2.5 Ghz (Python)
64
USVLab BSAODet
94.99 %
96.22 %
92.30 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
65
MVRA + I-FRCNN+
94.98 %
95.87 %
82.52 %
0.18 s
GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for
Orientation Estimation . The IEEE International Conference on
Computer Vision (ICCV) Workshops 2019.
66
SIENet
code
94.97 %
96.02 %
92.40 %
0.08 s
1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for
3D Object Detection from Point Cloud . 2021.
67
VueronNet
code
94.97 %
97.85 %
89.68 %
0.06 s
1 core @ 2.0 Ghz (Python)
68
VoTr-TSD
code
94.94 %
95.97 %
92.44 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection . ICCV 2021.
69
NV-RCNN
94.92 %
95.86 %
92.30 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
70
GVNet-V2
94.92 %
96.29 %
92.21 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
71
AGS-SSD[la]
94.90 %
96.18 %
92.07 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
72
TBD
code
94.90 %
95.98 %
92.11 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
73
GVNet
code
94.86 %
96.30 %
92.22 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
74
TBD
94.85 %
97.04 %
92.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
75
M3DeTR
code
94.83 %
97.39 %
92.10 %
n/a s
GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi-
scale, Mutual-relation 3D Object Detection with
Transformers . 2021.
76
StructuralIF
94.81 %
96.14 %
92.12 %
0.02 s
8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D
object detection on LiDAR-camera system . Accepted in CVIU 2021.
77
CSVoxel-RCNN
94.81 %
96.23 %
92.09 %
0.03 s
GPU @ 1.0 Ghz (Python)
78
SRIF-RCNN
94.79 %
95.63 %
92.35 %
0.0947 s
1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different
Sensors for 3D Object Detection . Applied Intelligence 2022.
79
ST-RCNN
94.79 %
98.06 %
92.12 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
80
DCCA
94.77 %
95.75 %
92.27 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
81
VCRCNN
94.77 %
96.06 %
92.28 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
82
XView
94.77 %
95.89 %
92.23 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D
Object Detector . 2021.
83
TBD
94.74 %
95.88 %
91.96 %
0.06 s
GPU @ 2.5 Ghz (Python)
84
P2V-RCNN
94.73 %
96.03 %
92.34 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature
Learning for 3D Object Detection from Point
Clouds . IEEE Access 2021.
85
FusionDetv2-v4
94.73 %
95.94 %
92.00 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
86
SC-Voxel-RCNN
94.71 %
96.13 %
91.94 %
0.12 s
GPU @ 1.0 Ghz (Python)
87
SPG
code
94.71 %
97.80 %
92.19 %
0.09 s
1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for
3D Object Detection via Semantic Point
Generation . Proceedings of the IEEE conference on
computer vision and pattern recognition (ICCV) 2021.
88
CAT-Det
94.71 %
95.97 %
92.07 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer
for Multi-modal 3D Object Detection . CVPR 2022.
89
MMLab PV-RCNN
code
94.70 %
98.17 %
92.04 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set
Abstraction for
3D Object Detection . CVPR 2020.
90
DKDet
94.68 %
96.03 %
91.92 %
0.03 s
GPU @ 2.5 Ghz (Python + C/C++)
91
SVGA-Net
94.67 %
96.05 %
91.86 %
0.03s
1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention
Network for 3D Object Detection from Point
Clouds . AAAI 2022.
92
DDet
94.66 %
95.82 %
92.17 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
93
FPV-SSD
94.66 %
96.92 %
91.78 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
94
DSA-PV-RCNN
code
94.64 %
95.86 %
92.10 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection . 2021.
95
RangeIoUDet
94.61 %
95.74 %
91.98 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time
3D
Object Detector Optimized by Intersection Over
Union . CVPR 2021.
96
USVLab BSAODet (S)
94.60 %
96.10 %
90.03 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
97
DVFENet
94.57 %
95.35 %
91.77 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature
Extraction Network for 3D Object Detection . Neurocomputing 2021.
98
IKT3D
94.57 %
95.77 %
91.97 %
0.05 s
1 core @ 2.5 Ghz (Python)
99
WGVRF
94.50 %
95.97 %
90.06 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
100
SPVB-SSD
94.49 %
95.80 %
91.90 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
101
CZY
94.47 %
96.00 %
90.08 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
102
MVMM
code
94.47 %
95.76 %
89.98 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
103
TuSimple
code
94.47 %
95.12 %
86.45 %
1.6 s
GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector
with scale dependent pooling and cascaded rejection classifiers . Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition . Proceedings of the IEEE conference on computer vision
and pattern recognition 2016.
104
EPNet
code
94.44 %
96.15 %
89.99 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection . ECCV 2020.
105
SERCNN
94.42 %
96.33 %
89.96 %
0.1 s
1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and
Object Detection for Autonomous Driving . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2020.
106
DCAN-Second
code
94.41 %
97.08 %
91.37 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
107
TCDVF
94.31 %
95.02 %
91.33 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
108
UberATG-MMF
94.25 %
97.41 %
89.87 %
0.08 s
GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D
Object Detection . CVPR 2019.
109
SRDL
94.24 %
95.86 %
91.80 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
110
FusionDetv1
94.23 %
95.84 %
91.80 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
111
TBD
94.22 %
95.00 %
91.35 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
112
SARFE
94.18 %
95.74 %
91.57 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
113
NV2P-RCNN
94.07 %
97.82 %
91.20 %
0.1 s
GPU @ 2.5 Ghz (Python)
114
DGT-Det3D
94.03 %
95.55 %
91.58 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
115
RangeRCNN
94.03 %
95.48 %
91.74 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D
Object Detection with Range Image
Representation . arXiv preprint arXiv:2009.00206 2020.
116
VueronNet
94.03 %
96.70 %
87.58 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
117
DGIST MT-CNN
94.00 %
95.25 %
86.76 %
0.09 s
GPU @ 1.0 Ghz (Python)
118
Faraway-Frustum
code
93.99 %
95.81 %
91.72 %
0.1 s
GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion . 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
119
DD3D
code
93.99 %
94.69 %
89.37 %
n/a s
1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D
Object detection? . IEEE/CVF International Conference on
Computer Vision (ICCV) .
120
SIF
93.95 %
95.51 %
91.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
P. An: SIF . Submitted to CVIU 2021.
121
TBD
93.89 %
94.52 %
86.44 %
0.1 s
1 core @ 2.5 Ghz (Python)
122
MGAF-3DSSD
code
93.87 %
94.45 %
86.37 %
0.1 s
1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage
Detector with Mask-Guided Attention for Point
Cloud . MM '21: The 29th ACM
International Conference on Multimedia (ACM MM) 2021.
123
LPCG-Monoflex
93.86 %
96.90 %
83.94 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
124
MMLAB LIGA-Stereo
code
93.82 %
96.43 %
86.19 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry
Aware Representations for Stereo-based 3D
Detector . Proceedings of the IEEE/CVF
International Conference on Computer Vision
(ICCV) 2021.
125
DKAnet
93.79 %
95.16 %
89.27 %
0.05 s
1 core @ 2.0 Ghz (Python)
126
Sem-Aug
93.77 %
96.79 %
88.78 %
0.08 s
GPU @ 2.5 Ghz (Python)
127
CF-ctdep-tv-ta
93.75 %
95.08 %
91.08 %
1 s
1 core @ 2.5 Ghz (C/C++)
128
Patches - EMP
93.75 %
97.91 %
90.56 %
0.5 s
GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D
Object Detection . arXiv preprint arXiv:1910.04093 2019.
129
CAD
93.73 %
96.84 %
88.74 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
130
CIA-SSD
code
93.72 %
96.87 %
86.20 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage
Object Detector From Point Cloud . AAAI 2021.
131
Anonymous
93.68 %
95.19 %
91.12 %
1
1 core @ 2.5 Ghz (Python)
132
CF-base-tv
93.68 %
94.86 %
90.87 %
1 s
1 core @ 2.5 Ghz (C/C++)
133
DTFI
93.67 %
95.17 %
91.10 %
0.03 s
1 core @ 2.5 Ghz (Python)
134
QD-3DT
code
93.66 %
94.26 %
83.63 %
0.03 s
GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking . ArXiv:2103.07351 2021.
135
MVAF-Net
code
93.66 %
95.37 %
90.90 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for
3D Object Detection . arXiv preprint arXiv:2011.00652 2020.
136
SSL-PointGNN
code
93.65 %
96.61 %
88.53 %
0.56 s
GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow
Backbone . arXiv preprint arXiv:2205.00705 2022.
137
SA3DNet
93.62 %
96.57 %
88.65 %
0.05 s
GPU @ 2.5 Ghz (Python)
138
FusionDetv2-v5
93.61 %
95.33 %
89.22 %
0.05 s
1 core @ 2.5 Ghz (Java + C/C++)
139
KpNet
93.60 %
96.76 %
85.98 %
0.42 s
1 core @ 2.5 Ghz (C/C++)
140
KpNet
93.60 %
96.74 %
85.97 %
42 s
1 core @ 2.5 Ghz (C/C++)
141
TF3D
93.57 %
96.59 %
88.64 %
0.1 s
2 cores @ 3.0 Ghz (Python)
142
DVF
93.57 %
96.46 %
88.58 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
143
IA-SSD (multi)
code
93.56 %
96.10 %
90.68 %
0.014 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly
Efficient Point-based Detectors for 3D LiDAR Point
Clouds . CVPR 2022.
144
MonoPair
93.55 %
96.61 %
83.55 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection
Using Pairwise Spatial Relationships . The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR) 2020.
145
IA-SSD (single)
code
93.54 %
96.26 %
88.49 %
0.013 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly
Efficient Point-based Detectors for 3D LiDAR Point
Clouds . CVPR 2022.
146
EBM3DOD
code
93.54 %
96.81 %
88.33 %
0.12 s
1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy-
Based Models . arXiv preprint arXiv:2012.04634 2020.
147
Deep MANTA
93.50 %
98.89 %
83.21 %
0.7 s
GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image . CVPR 2017.
148
Point-GNN
code
93.50 %
96.58 %
88.35 %
0.6 s
GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D
Object Detection in a Point Cloud . CVPR 2020.
149
FV2P v2
93.49 %
94.33 %
90.85 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
150
BtcDet
code
93.47 %
96.23 %
88.55 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded
Shapes for 3D Object Detection . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
151
Struc info fusion II
93.45 %
96.72 %
88.31 %
0.05 s
GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion . Submitted to CVIU 2021.
152
DGCN
93.45 %
96.35 %
90.35 %
0.1 s
GPU @ 2.5 Ghz (Python)
153
EBM3DOD baseline
code
93.45 %
96.72 %
88.25 %
0.05 s
1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy-
Based Models . arXiv preprint arXiv:2012.04634 2020.
154
StereoDistill
93.43 %
97.61 %
87.71 %
0.4 s
1 core @ 2.5 Ghz (Python)
155
RRC
code
93.40 %
95.68 %
87.37 %
3.6 s
GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using
Recurrent Rolling Convolution . CVPR 2017.
156
Sem-Aug v1
code
93.39 %
96.39 %
90.70 %
0.04 s
GPU @ 3.5 Ghz (Python)
157
3D-CVF at SPA
93.36 %
96.78 %
86.11 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and
LiDAR
Features Using Cross-View Spatial Feature
Fusion for
3D Object Detection . ECCV 2020.
158
PVTr
93.36 %
94.54 %
90.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
159
Anonymous
93.34 %
96.44 %
83.76 %
40 s
1 core @ 2.5 Ghz (C/C++)
160
DSASNet
93.33 %
96.55 %
88.47 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
161
3SNet
93.33 %
96.40 %
90.65 %
0.07 s
GPU @ 2.5 Ghz (Python)
162
SNVC
code
93.32 %
96.33 %
85.81 %
1 s
GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
163
FS-Net
93.32 %
96.39 %
90.64 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
164
CF-ctdep-tv
93.31 %
94.89 %
90.87 %
1 s
1 core @ 2.5 Ghz (C/C++)
165
Anonymous
93.31 %
95.92 %
85.44 %
0.03 s
GPU @ >3.5 Ghz (Python)
166
Struc info fusion I
93.31 %
96.59 %
88.23 %
0.05 s
1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion . Submitted to CVIU 2021.
167
VPN
93.30 %
96.19 %
88.30 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
168
CityBrainLab-CT3D
code
93.30 %
96.28 %
90.58 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel-
wise Transformer . ICCV 2021.
169
MonoInsight
93.29 %
96.21 %
83.59 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
170
ITCA-SSD
code
93.27 %
96.65 %
88.14 %
0.05 s
1 core @ 2.5 Ghz (Python)
171
Reprod-Two-Branch
93.26 %
94.83 %
90.61 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
172
SPNet
code
93.23 %
95.99 %
92.60 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
173
STD
code
93.22 %
96.14 %
90.53 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for
Point Cloud . ICCV 2019.
174
SARPNET
93.21 %
96.07 %
88.09 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal
Network for LiDAR-based 3D Object Detection . Neurocomputing 2019.
175
CFF-ep25
93.20 %
94.81 %
90.76 %
1 s
1 core @ 2.5 Ghz (C/C++)
176
H^23D R-CNN
code
93.20 %
96.20 %
90.55 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated
Hollow-3D R-CNN for 3D Object Detection . IEEE Transactions on Circuits and Systems
for Video Technology 2021.
177
Fast Point R-CNN
93.18 %
96.13 %
87.68 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN . Proceedings of the IEEE international
conference on computer vision (ICCV) 2019.
178
sensekitti
code
93.17 %
94.79 %
84.38 %
4.5 s
GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images . CVPR 2016.
179
Sem-Aug-PointRCNN
code
93.17 %
95.78 %
88.35 %
0.1 s
GPU @ 3.5 Ghz (C/C++)
180
SJTU-HW
93.11 %
96.30 %
82.21 %
0.85s
GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION
EMBEDDED DETECTOR . IEEE International Conference on
Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection
based on shifted single shot detector . Multimedia Tools and Applications 2018.
181
SGNet
93.08 %
96.43 %
90.53 %
0.09 s
GPU @ 2.5 Ghz (Python)
182
FromVoxelToPoint
code
93.06 %
96.08 %
90.53 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D
Object Detection for Point Cloud with Voxel-to-
Point Decoder . MM '21: The 29th ACM
International Conference on Multimedia (ACM MM) 2021.
183
CFF-tv
93.06 %
94.68 %
90.61 %
1 s
1 core @ 2.5 Ghz (C/C++)
184
cp-tv
92.99 %
94.52 %
90.36 %
1 s
1 core @ 2.5 Ghz (C/C++)
185
Anonymous
92.99 %
96.18 %
87.80 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
186
MSADet
92.95 %
96.18 %
89.95 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
187
CLOCs_SecCas
92.95 %
95.43 %
89.21 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection . 2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
188
cff-tv-v2-ep25
92.94 %
94.65 %
90.62 %
1 s
1 core @ 2.5 Ghz (C/C++)
189
GT3D
92.93 %
96.35 %
90.24 %
0.1 s
1 core @ 2.5 Ghz (Python)
190
KPP3D
code
92.93 %
98.38 %
89.93 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
191
mbdf-netv1
code
92.83 %
96.00 %
89.90 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
192
CF-base-train
92.82 %
94.98 %
90.19 %
1 s
1 core @ 2.5 Ghz (C/C++)
193
MDNet
92.82 %
96.14 %
85.37 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
194
HotSpotNet
92.81 %
96.21 %
89.80 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots . Proceedings of the European Conference
on Computer Vision (ECCV) 2020.
195
SegVoxelNet
92.73 %
96.00 %
87.60 %
0.04 s
1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context
and
Depth-aware Features for 3D Vehicle Detection from
Point Cloud . ICRA 2020.
196
Patches
92.72 %
96.34 %
87.63 %
0.15 s
GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D
Object Detection . arXiv preprint arXiv:1910.04093 2019.
197
CenterNet3D
92.69 %
95.76 %
89.81 %
0.04 s
GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous
Driving . 2020.
198
R-GCN
92.67 %
96.19 %
87.66 %
0.16 s
GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
199
PI-RCNN
92.66 %
96.17 %
87.68 %
0.1 s
1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D
Object Detector with Point-based Attentive Cont-conv
Fusion Module . AAAI 2020 : The Thirty-Fourth
AAAI Conference on Artificial Intelligence 2020.
200
PointPainting
92.58 %
98.39 %
89.71 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object
Detection . CVPR 2020.
201
DASS
92.53 %
96.23 %
87.75 %
0.09 s
1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic
Segmentation by Learning 3D Object Detection . Proceedings of the IEEE/CVF
Winter Conference on Applications of Computer
Vision (WACV) 2021.
202
Anonymous
92.51 %
95.88 %
85.35 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
203
3D IoU-Net
92.47 %
96.31 %
87.67 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for
Point Clouds . arXiv preprint arXiv:2004.04962 2020.
204
CSNet8306
code
92.47 %
96.05 %
87.61 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
205
CSNet
92.46 %
95.99 %
89.24 %
0.1 s
1 core @ 2.5 Ghz (Python)
206
Associate-3Ddet
code
92.45 %
95.61 %
87.32 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual
Association for 3D Point Cloud Object Detection . The IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2020.
207
S-AT GCN
92.44 %
95.06 %
90.78 %
0.02 s
GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention
Graph Convolution Network based Feature
Enhancement for 3D Object
Detection . CoRR 2021.
208
DD3Dv2
code
92.41 %
95.41 %
87.27 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
209
LazyTorch-CP-Small-P
92.38 %
94.71 %
90.04 %
1 s
1 core @ 2.5 Ghz (C/C++)
210
LazyTorch-CP-Infer-O
92.35 %
94.76 %
90.09 %
1 s
1 core @ 2.5 Ghz (C/C++)
211
cp-tv-kp
92.34 %
94.32 %
90.25 %
1 s
1 core @ 2.5 Ghz (C/C++)
212
PointRGCN
92.33 %
97.51 %
87.07 %
0.26 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
213
Sem-Aug-PointRCNN++
92.32 %
95.65 %
87.62 %
0.1 s
8 cores @ 3.0 Ghz (Python)
214
CSNet8299
code
92.31 %
96.14 %
87.54 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
215
PSM_stereo
92.27 %
95.63 %
87.24 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
216
CenterFuse
92.26 %
95.04 %
89.24 %
0.059 sec/frame
2 x V100
217
F-ConvNet
code
92.19 %
95.85 %
80.09 %
0.47 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to
Aggregate Local Point-Wise Features for Amodal 3D
Object Detection . IROS 2019.
218
Self-Calib Conv
92.17 %
94.45 %
90.19 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
219
PSA-Det3D
92.17 %
95.53 %
89.67 %
0.1 s
GPU @ 2.5 Ghz (Python)
220
PFF3D
code
92.15 %
95.37 %
87.54 %
0.05 s
GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and
Accurate 3D Object Detection for Lidar-Camera-Based
Autonomous Vehicles Using One Shared Voxel-Based
Backbone . IEEE Access 2021.
221
AFTD
92.14 %
95.56 %
87.45 %
1 s
1 core @ 2.5 Ghz (Python + C/C++)
222
cp-tv-kp-io-sc
92.11 %
95.02 %
88.97 %
1 s
1 core @ 2.5 Ghz (C/C++)
223
CF-cd-io-tv
92.06 %
94.80 %
88.80 %
1 s
1 core @ 2.5 Ghz (C/C++)
224
SSL_PP
code
92.04 %
95.97 %
84.91 %
16ms
GPU @ 1.5 Ghz (Python)
225
SDP+RPN
92.03 %
95.16 %
79.16 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN
Object Detector with Scale Dependent Pooling and
Cascaded Rejection Classifiers . Proceedings of the IEEE International
Conference on Computer Vision and Pattern
Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection
with region proposal networks . Advances in Neural Information Processing
Systems 2015.
226
KeyFuse2B
92.01 %
94.57 %
90.56 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
227
AB3DMOT
code
92.00 %
95.88 %
86.98 %
0.0047s
1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object
Tracking . arXiv:1907.03961 2019.
228
CrazyTensor-CP
91.95 %
93.64 %
89.23 %
1 s
1 core @ 2.5 Ghz (Python)
229
ZMMPP
91.92 %
94.93 %
88.86 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
230
CFF-tv-v2
91.91 %
94.72 %
90.57 %
1 s
1 core @ 2.5 Ghz (C/C++)
231
Dune-DCF-e09
91.90 %
94.97 %
88.74 %
1 s
1 core @ 2.5 Ghz (C/C++)
232
MMLab-PointRCNN
code
91.90 %
95.92 %
87.11 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation
and
detection from point cloud . Proceedings of the IEEE Conference
on
Computer Vision and Pattern Recognition 2019.
233
CrazyTensor-CF
91.89 %
94.98 %
88.70 %
1 s
1 core @ 2.5 Ghz (C/C++)
234
AutoAlign
91.87 %
95.15 %
89.21 %
0.1 s
1 core @ 2.5 Ghz (Python)
235
MMLab-PartA^2
code
91.86 %
95.03 %
89.06 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from
Point Cloud with Part-aware and Part-aggregation
Network . IEEE Transactions on Pattern Analysis and
Machine Intelligence 2020.
236
WeakM3D
code
91.81 %
94.51 %
85.35 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised
Monocular 3D Object Detection . ICLR 2022.
237
KeyPoint-IoUHead
91.79 %
94.65 %
88.61 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
238
epBRM
code
91.77 %
94.59 %
88.45 %
0.1 s
GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection
by Spatial Transformation Mechanism . arXiv preprint arXiv:1910.04853 2019.
239
TBD
91.75 %
94.53 %
86.54 %
0.1 s
1 core @ 2.5 Ghz (Python)
240
C-GCN
91.73 %
95.64 %
86.37 %
0.147 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D
Vehicles Detection Refinement . ArXiv 2019.
241
ITVD
code
91.73 %
95.85 %
79.31 %
0.3 s
GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in
Complex Scenes . IEEE International Conference on
Multimedia and Expo (ICME) 2018.
242
Dune-DCF-e11
91.68 %
94.69 %
88.57 %
1 s
1 core @ 2.5 Ghz (C/C++)
243
City-CF-fixed
91.67 %
94.87 %
88.80 %
1 s
1 core @ 2.5 Ghz (C/C++)
244
Dune-DCF-e15
91.67 %
94.72 %
88.53 %
1 s
1 core @ 2.5 Ghz (C/C++)
245
SINet+
code
91.67 %
94.17 %
78.60 %
0.3 s
TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional
Neural Network for Fast Vehicle Detection . IEEE Transactions on Intelligent
Transportation Systems 2019.
246
SFD-Retina
91.64 %
94.85 %
82.13 %
0.04 s
GPU @ 2.5 Ghz (Python)
247
CF-ctdep-train
91.64 %
94.81 %
90.16 %
1 s
1 core @ 2.5 Ghz (C/C++)
248
HS3D
code
91.62 %
95.51 %
86.94 %
0.12 s
1 core @ 2.5 Ghz (Python + C/C++)
249
mono3d
91.60 %
94.60 %
84.86 %
0.03 s
GPU @ 2.5 Ghz (Python)
250
Cascade MS-CNN
code
91.60 %
94.26 %
78.84 %
0.25 s
GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object
Detection and Instance Segmentation . arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep
convolutional neural network for fast object
detection . European conference on computer
vision 2016.
251
GFD-Retina
91.59 %
94.55 %
81.80 %
0.07 s
GPU @ 2.5 Ghz (Python)
252
CenterPoint (pcdet)
91.58 %
93.99 %
89.15 %
0.051 sec/frame
2 x V100
253
TBD
91.56 %
94.52 %
86.29 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
254
PointRGBNet
91.48 %
95.40 %
86.50 %
0.08 s
4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects
Based on Multi-Sensor Information Fusion . Automotive Engineering 2022.
255
MAFF-Net(DAF-Pillar)
91.46 %
94.38 %
83.89 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D
Vehicle Detection with Multi-modal Adaptive Feature
Fusion . arXiv preprint arXiv:2009.10945 2020.
256
HRI-VoxelFPN
91.44 %
96.65 %
86.18 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature
aggregation in 3D object detection from point
clouds . sensors 2020.
257
new_stereo
91.39 %
95.01 %
86.77 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
258
EgoNet
code
91.39 %
96.18 %
81.33 %
0.1 s
GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation
for monocular vehicle pose estimation . The IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2021.
259
IoU-2B
91.38 %
94.81 %
85.63 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
260
T_PVRCNN
91.36 %
95.02 %
88.45 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
261
GPENet
code
91.36 %
94.11 %
83.40 %
0.02 s
GPU @ 2.5 Ghz (Python)
262
City-CF
91.35 %
94.54 %
88.38 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
263
PP-PCdet
code
91.32 %
94.82 %
88.18 %
0.01 s
1 core @ 2.5 Ghz (Python)
264
T_PVRCNN_V2
91.31 %
95.59 %
88.22 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
265
CZY_3917
91.29 %
94.94 %
86.73 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
266
Contrastive PP
code
91.29 %
96.93 %
88.11 %
0.01 s
1 core @ 2.5 Ghz (Python)
267
Stereo CenterNet
91.27 %
96.61 %
83.50 %
0.04 s
GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object
detection for autonomous driving . Neurocomputing 2022.
268
PointPillars
code
91.19 %
94.00 %
88.17 %
16 ms
1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from
Point Clouds . CVPR 2019.
269
LTN
91.18 %
94.68 %
81.51 %
0.4 s
GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for
Context Aware Object Detection . IEEE Transactions on Intelligent
Transportation Systems 2019.
270
WS3D
91.15 %
95.13 %
86.52 %
0.1 s
GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection
from Lidar Point Cloud . 2020.
271
KM3D
code
91.07 %
96.44 %
81.19 %
0.03 s
1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training . 2020.
272
PS++
code
91.06 %
96.23 %
83.39 %
PS++ s
1 core @ 2.5 Ghz (C/C++)
273
3D_att
91.05 %
96.70 %
85.88 %
0.17 s
GPU @ 2.5 Ghz (Python)
274
DID-M3D
91.04 %
94.29 %
81.31 %
0.04 s
1 core @ 2.5 Ghz (Python)
275
FII-CenterNet
code
91.03 %
94.48 %
83.00 %
0.09 s
GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector
With Foreground Attention for Traffic Object
Detection . IEEE Transactions on Vehicular
Technology 2021.
276
Aston-EAS
91.02 %
93.91 %
77.93 %
0.24 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance . IEEE Transactions on Intelligent Transportation Systems 2019.
277
MonoFlex
91.02 %
96.01 %
83.38 %
0.03 s
GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D
Object Detection . CVPR 2021.
278
Mix-Teaching
91.02 %
96.35 %
83.41 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
279
SCSTSV-MonoFlex
90.99 %
96.44 %
81.16 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
280
ARPNET
90.99 %
94.00 %
83.49 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network
for 3D object detection . Science China Information Sciences 2019.
281
VMDet
90.98 %
96.31 %
83.43 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
282
cff-tv-t
90.98 %
94.68 %
84.39 %
1 s
1 core @ 2.5 Ghz (C/C++)
283
monopd
code
90.93 %
96.44 %
83.36 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
284
DDS
code
90.93 %
96.44 %
83.36 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
285
MoGDE
90.91 %
96.47 %
83.66 %
0.03 s
GPU @ 2.5 Ghz (Python)
286
MonoEF
90.88 %
96.32 %
83.27 %
0.03 s
1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An
Extrinsic Parameter Free Approach . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2021.
287
PatchNet
code
90.87 %
93.82 %
79.62 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation . Proceedings of the European Conference
on Computer Vision (ECCV) 2020.
288
PS
code
90.85 %
96.20 %
83.08 %
PS s
1 core @ 2.5 Ghz (C/C++)
289
MV3D
90.83 %
96.47 %
78.63 %
0.36 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for
Autonomous
Driving . CVPR 2017.
290
MonoDistill
90.81 %
95.92 %
81.08 %
0.04 s
1 core @ 2.5 Ghz (Python)
291
monodle
code
90.81 %
93.83 %
80.93 %
0.04 s
GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for
Monocular 3D Object Detection . CVPR 2021 .
292
3D IoU Loss
90.79 %
95.92 %
85.65 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection . International Conference on 3D
Vision
(3DV) 2019.
293
SINet_VGG
code
90.79 %
93.59 %
77.53 %
0.2 s
TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional
Neural Network for Fast Vehicle Detection . IEEE Transactions on Intelligent
Transportation Systems 2019.
294
Anonymous
90.78 %
96.42 %
83.51 %
40 s
1 core @ 2.5 Ghz (C/C++)
295
HomoLoss(monoflex)
code
90.69 %
95.92 %
80.91 %
0.04 s
1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object
Detection . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
296
TANet
code
90.67 %
93.67 %
85.31 %
0.035s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from
Point Clouds with Triple Attention . AAAI 2020.
297
MF
90.66 %
93.57 %
86.15 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
298
MonoGround
90.63 %
93.94 %
80.80 %
0.03 s
1 core @ 2.5 Ghz (Python)
299
MonoEdge
90.62 %
93.52 %
80.91 %
0.05 s
GPU @ 2.5 Ghz (Python)
300
MonoCInIS
90.60 %
96.05 %
82.43 %
0,13 s
GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular
3D Object Detection using Instance Segmentation . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2021.
301
MonoFlex
90.52 %
95.59 %
83.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
302
CG-Stereo
90.38 %
96.31 %
82.80 %
0.57 s
GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object
Detection with
Split Depth Estimation . IROS 2020.
303
MonoEdge-Rotate
90.30 %
93.53 %
80.60 %
0.05 s
GPU @ 2.5 Ghz (Python)
304
SCNet
90.30 %
95.59 %
85.09 %
0.04 s
GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud . IEEE Access 2019.
305
CMKD*
90.28 %
95.14 %
83.91 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
306
PS-fld
code
90.27 %
95.75 %
82.32 %
0.25 s
1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object
Detection in Autonomous Driving . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
307
MonoAug
90.24 %
95.59 %
80.47 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
308
Deep3DBox
90.19 %
94.71 %
76.82 %
1.5 s
GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep
Learning and Geometry . CVPR 2017.
309
FQNet
90.17 %
94.72 %
76.78 %
0.5 s
1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for
Monocular 3D Object Detection . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 2019.
310
DeepStereoOP
90.06 %
95.15 %
79.91 %
3.4 s
GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for
Object Detection in Autonomous Driving Using
Convolutional Neural Networks . Signal Processing: Image
Communiation 2017.
311
variance_point
90.01 %
95.79 %
87.50 %
0.05 s
1 core @ 2.5 Ghz (Python)
312
SubCNN
89.98 %
94.26 %
79.78 %
2 s
GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural
Networks for Object Proposals and Detection . IEEE Winter Conference on Applications of
Computer Vision (WACV) 2017.
313
MLOD
code
89.97 %
94.88 %
84.98 %
0.12 s
GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method . arXiv preprint arXiv:1909.04163 2019.
314
GPP
code
89.96 %
94.02 %
81.13 %
0.23 s
GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose
estimation of objects on the road . IEEE Transactions on Intelligent
Vehicles 2020.
315
AVOD
code
89.88 %
95.17 %
82.83 %
0.08 s
Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object
Detection from View Aggregation . IROS 2018.
316
SINet_PVA
code
89.86 %
92.72 %
76.47 %
0.11 s
TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional
Neural Network for Fast Vehicle Detection . IEEE Transactions on Intelligent
Transportation Systems 2019.
317
Digging_M3D
89.77 %
93.73 %
79.68 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
318
TBD_BD
code
89.73 %
94.18 %
86.84 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
319
3DOP
code
89.55 %
92.96 %
79.38 %
3s
GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class
Detection . NIPS 2015.
320
IAFA
89.46 %
93.08 %
79.83 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation
for 3D Object Detection from a Single Image . Proceedings of the Asian Conference on
Computer Vision 2020.
321
Mono3D
code
89.37 %
94.52 %
79.15 %
4.2 s
GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous
Driving . CVPR 2016.
322
4d-MSCNN
code
89.37 %
92.40 %
77.00 %
0.3 min
GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision . IET Intelligent Transport Systems 2020.
323
MonoDDE
89.19 %
96.76 %
81.60 %
0.04 s
1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth
Clues for Reliable Monocular 3D Object Detection . CVPR 2022.
324
M3DSSD++
code
89.06 %
94.94 %
77.17 %
0.16s
1 core @ 2.5 Ghz (C/C++)
325
MonoEdge-RCNN
88.97 %
94.19 %
74.23 %
0.05 s
1 core @ 2.5 Ghz (Python)
326
AVOD-FPN
code
88.92 %
94.70 %
84.13 %
0.1 s
Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation . IROS 2018.
327
Keypoint-3D
88.87 %
93.31 %
76.10 %
14 s
1 core @ 2.5 Ghz (C/C++)
328
Shape-Aware
88.85 %
94.26 %
81.33 %
0.05 s
1 core @ 2.5 Ghz (Python)
329
PCT
code
88.78 %
96.45 %
78.85 %
0.045 s
1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for
Monocular 3D Object Detection . NeurIPS 2021.
330
OPA-3D
code
88.77 %
96.50 %
76.55 %
0.04 s
1 core @ 3.5 Ghz (Python)
331
FusionDetv2-baseline
88.76 %
94.28 %
85.65 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
332
Lite-FPN-GUPNet
88.76 %
96.45 %
76.54 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
333
AM3D
88.71 %
92.55 %
77.78 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color-
Embedded 3D Reconstruction for Autonomous Driving . Proceedings of the IEEE international
Conference on Computer Vision (ICCV) 2019.
334
MS-CNN
code
88.68 %
93.87 %
76.11 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep
Convolutional Neural Network for Fast Object
Detection . ECCV 2016.
335
MM-Retina
88.63 %
93.74 %
78.56 %
0.04 s
GPU @ 2.5 Ghz (Python)
336
MonoPSR
code
88.50 %
93.63 %
73.36 %
0.2 s
GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging
Accurate Proposals and Shape Reconstruction . CVPR 2019.
337
Shift R-CNN (mono)
code
88.48 %
94.07 %
78.34 %
0.25 s
GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D
Object Detection With Closed-form Geometric
Constraints . ICIP 2019.
338
RCD
88.46 %
92.52 %
83.73 %
0.1 s
GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for
Scale Invariant 3D Object Detection . Conference on Robot Learning (CoRL) 2020.
339
MM-MRFC
88.46 %
95.54 %
78.14 %
0.05 s
GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast
Boosting based Detection using Scale Invariant
Multimodal Multiresolution Filtered Features . CVPR 2017.
340
MonoDTR
88.41 %
93.90 %
76.20 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with
Depth-Aware Transformer . CVPR 2022.
341
anonymity
88.41 %
95.28 %
81.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
342
anonymity
88.40 %
95.03 %
81.67 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
343
MonoFar
88.33 %
93.74 %
78.59 %
0.04 s
1 core @ 2.5 Ghz (Python)
344
3DBN
88.29 %
93.74 %
80.74 %
0.13s
1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object
Detection . CoRR 2019.
345
MonoCon
code
88.22 %
93.59 %
76.18 %
0.02 s
GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps
Monocular 3D Object Detection . AAAI 2022.
346
MonoCInIS
88.16 %
96.22 %
75.72 %
0,14 s
GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular
3D Object Detection using Instance Segmentation . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2021.
347
DEPT
88.00 %
96.45 %
78.40 %
0.03 s
1 core @ 2.5 Ghz (Python)
348
EW
code
87.94 %
92.22 %
78.32 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
349
MonoRUn
code
87.91 %
95.48 %
78.10 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation . IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
350
SAIC_ADC_Mono3D
code
87.78 %
95.21 %
80.14 %
50 s
GPU @ 2.5 Ghz (Python)
351
MonoAug
87.76 %
93.65 %
77.91 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
352
CZY
87.55 %
94.63 %
82.97 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
353
SMOKE
code
87.51 %
93.21 %
77.66 %
0.03 s
GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object
Detection via Keypoint Estimation . 2020.
354
zongmuDistill
87.47 %
95.44 %
79.97 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
355
CDN
code
87.19 %
95.85 %
79.43 %
0.6 s
GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo
Disparity Estimation . Advances in Neural
Information Processing Systems (NeurIPS) 2020.
356
mono3d
code
87.07 %
93.28 %
77.72 %
TBD
TBD
357
BiResFPN
87.06 %
86.03 %
75.85 %
0.071s
1 core @ 2.5 Ghz (C/C++)
358
RTM3D
code
86.93 %
91.82 %
77.41 %
0.05 s
GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection
from Object Keypoints for Autonomous Driving . 2020.
359
MonoRCNN
code
86.78 %
91.98 %
66.97 %
0.07 s
GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition
for Monocular 3D Object Detection . ICCV 2021.
360
Anonymous
code
86.77 %
94.34 %
71.93 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
361
BirdNet+
code
86.73 %
92.61 %
81.80 %
0.11 s
Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection
in LiDAR through a Sparsity-Invariant Bird’s Eye
View . IEEE Access 2021.
362
UPF_3D
86.61 %
96.12 %
79.53 %
0.29 s
1 core @ 2.5 Ghz (Python)
363
MP-Mono
86.54 %
90.66 %
65.72 %
0.16 s
GPU @ 2.5 Ghz (Python)
364
MK3D
86.48 %
94.40 %
74.21 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
365
ANM
86.45 %
94.32 %
76.54 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
366
GUPNet
code
86.45 %
94.15 %
74.18 %
NA s
1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network
for Monocular 3D Object Detection . arXiv preprint arXiv:2107.13774 2021.
367
GCDR
86.45 %
94.15 %
74.18 %
0.28 s
1 core @ 2.5 Ghz (Python)
368
DSGN
code
86.43 %
95.53 %
78.75 %
0.67 s
NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D
Object Detection . CVPR 2020.
369
gupnet_se
86.33 %
94.27 %
74.08 %
0.03s
1 core @ 2.5 Ghz (C/C++)
370
OBMO_GUPNet
86.27 %
96.49 %
76.47 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
371
MonoDETR
code
86.17 %
93.99 %
76.19 %
0.04 s
1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for
Monocular 3D Object Detection . arXiv preprint arXiv:2203.13310 2022.
372
TBD
86.03 %
91.50 %
78.52 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
373
Stereo R-CNN
code
85.98 %
93.98 %
71.25 %
0.3 s
GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection
for
Autonomous Driving . CVPR 2019.
374
StereoFENet
85.70 %
91.48 %
77.62 %
0.15 s
1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection
with
Feature Enhancement Networks . IEEE Transactions on Image Processing 2019.
375
City-ICN
85.70 %
93.91 %
73.41 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
376
Anonymous
85.65 %
92.64 %
78.83 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
377
DMF
85.49 %
89.50 %
82.52 %
0.2 s
1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for
Transportation Detection . IEEE Transactions on Intelligent
Transportation Systems 2022.
378
LT-M3OD
85.42 %
93.99 %
75.77 %
0.03 s
1 core @ 2.5 Ghz (Python)
379
ResNet-RRC_Car
85.33 %
91.45 %
74.27 %
0.06 s
GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet-
Based Recurrent Rolling Convolution . Proceedings of the IEEE conference
on
systems, man, and cybernetics 2018.
380
SparseLiDAR_fusion
85.26 %
93.60 %
73.11 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
381
AEC3D
85.22 %
90.74 %
80.82 %
18 ms
GPU @ 2.5 Ghz (Python)
382
PL++ (SDN+GDC)
code
85.15 %
94.95 %
77.78 %
0.6 s
GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D
Object Detection in Autonomous Driving . International Conference on Learning
Representations 2020.
383
M3D-RPN
code
85.08 %
89.04 %
69.26 %
0.16 s
GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal
Network for Object Detection . ICCV 2019 .
384
CDN-PL++
85.01 %
94.66 %
77.60 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity
Estimation . Advances in Neural Information
Processing Systems 2020.
385
M3DGAF
85.01 %
93.33 %
77.55 %
0.07 s
1 core @ 2.5 Ghz (Python)
386
SDP+CRC (ft)
85.00 %
92.06 %
71.71 %
0.6 s
GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN
Object Detector with Scale Dependent Pooling and
Cascaded Rejection Classifiers . Proceedings of the IEEE International
Conference on Computer Vision and Pattern Recognition 2016.
387
SS3D
84.92 %
92.72 %
70.35 %
48 ms
Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained
End-to-End Using
Intersection-over-Union Loss . CoRR 2019.
388
ZongmuMono3d
code
84.64 %
93.06 %
75.29 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
389
MonoFENet
84.63 %
91.68 %
76.71 %
0.15 s
1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object
Detection
with Feature Enhancement Networks . IEEE Transactions on Image
Processing 2019.
390
DLE
code
84.45 %
94.66 %
62.10 %
0.06 s
NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction . Proceedings of the British Machine Vision Conference (BMVC) 2021.
391
MV3D (LIDAR)
84.39 %
93.08 %
79.27 %
0.24 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for
Autonomous
Driving . CVPR 2017.
392
Complexer-YOLO
84.16 %
91.92 %
79.62 %
0.06 s
GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object
Detection and Tracking on Semantic Point
Clouds . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)
Workshops 2019.
393
ZoomNet
code
83.92 %
94.22 %
69.00 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming
Neural Network for 3D Object Detection . Proceedings of the AAAI Conference on
Artificial Intelligence 2020.
394
CMAN
83.74 %
89.74 %
65.35 %
0.15 s
1 core @ 2.5 Ghz (Python)
395
D4LCN
code
83.67 %
90.34 %
65.33 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for
Monocular 3D Object Detection . CVPR 2020.
396
Faster R-CNN
code
83.16 %
88.97 %
72.62 %
2 s
GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real-
Time
Object Detection with Region Proposal
Networks . NIPS 2015.
397
ppt
83.13 %
85.27 %
77.48 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
398
SGM3D
83.05 %
93.66 %
73.35 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection . 2021.
399
Pseudo-LiDAR++
code
82.90 %
94.46 %
75.45 %
0.4 s
GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D
Object Detection in Autonomous Driving . International Conference on Learning
Representations 2020.
400
Disp R-CNN
code
82.86 %
93.64 %
68.33 %
0.387 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection
via Shape Prior Guided Instance Disparity
Estimation . CVPR 2020.
401
BS3D
82.72 %
95.35 %
70.01 %
22 ms
Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding
Shapes for Real-Time 3D Vehicle Detection from
Monocular RGB Images . 2019 IEEE Intelligent Vehicles
Symposium (IV) 2019.
402
CrazyTensor-ICN
82.70 %
90.79 %
70.61 %
1 s
1 core @ 2.5 Ghz (C/C++)
403
Disp R-CNN (velo)
code
82.64 %
93.45 %
70.45 %
0.387 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via
Shape Prior Guided Instance Disparity Estimation . CVPR 2020.
404
HBD
82.64 %
93.13 %
75.14 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
405
HomoLoss(imvoxelnet)
code
82.54 %
92.81 %
72.80 %
0.20 s
1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object
Detection . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
406
YOLOStereo3D
code
82.15 %
94.81 %
62.17 %
0.1 s
GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for
Efficient Stereo 3D Detection . 2021 International Conference on
Robotics and Automation (ICRA) 2021.
407
Ground-Aware
code
82.05 %
92.33 %
62.08 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object
Detection for Autonomous Driving . IEEE Robotics and Automation Letters 2021.
408
FRCNN+Or
code
82.00 %
92.91 %
68.79 %
0.09 s
Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding . IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features . IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
409
Anonymous
81.71 %
96.77 %
69.38 %
40 s
1 core @ 2.5 Ghz (C/C++)
410
SwinMono3D
81.71 %
91.99 %
61.78 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
411
DDMP-3D
81.70 %
91.15 %
63.12 %
0.18 s
1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for
Monocular 3D Object Detection . CVPR 2020.
412
SD3DOD
81.64 %
92.60 %
75.97 %
0.04 s
GPU @ 2.5 Ghz (Python)
413
A3DODWTDA (image)
code
81.25 %
78.96 %
70.56 %
0.8 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
414
none
81.07 %
91.14 %
68.85 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
415
ART
81.03 %
90.97 %
74.44 %
20ms s
1 core @ 2.5 Ghz (C/C++)
416
RefineNet
81.01 %
91.91 %
65.67 %
0.20 s
GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for
Autonomous Driving . IEEE Transactions on Intelligent
Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for
Accurate Object Localization . Intelligent Transportation Systems
Conference 2016.
417
K3D
80.86 %
93.58 %
71.18 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
418
CaDDN
code
80.73 %
93.61 %
71.09 %
0.63 s
GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution
Network for Monocular 3D Object Detection . CVPR 2021.
419
ESGN
80.58 %
93.07 %
70.68 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
420
PGD-FCOS3D
code
80.58 %
92.04 %
69.67 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth:
Detecting Objects in Perspective . Conference on Robot Learning
(CoRL) 2021.
421
FD
80.38 %
92.08 %
75.65 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
422
GrooMeD-NMS
code
80.28 %
90.14 %
63.78 %
0.12 s
1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection . CVPR 2021.
423
3D-GCK
80.19 %
89.55 %
68.08 %
24 ms
Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles
from Monocular RGB Images via Geometrically
Constrained Keypoints in Real-Time . 2020 IEEE Intelligent Vehicles
Symposium (IV) 2020.
424
COF3D
80.16 %
87.85 %
61.97 %
200 s
1 core @ 2.5 Ghz (Python + C/C++)
425
EM
code
80.05 %
87.00 %
66.77 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
426
Lite-FPN
79.65 %
87.04 %
65.56 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
427
YoloMono3D
code
79.63 %
92.37 %
59.69 %
0.05 s
GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for
Efficient Stereo 3D Detection . 2021 International Conference on
Robotics and Automation (ICRA) 2021.
428
A3DODWTDA
code
79.15 %
82.98 %
68.30 %
0.08 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
429
ImVoxelNet
code
79.09 %
89.80 %
69.45 %
0.2 s
GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection . arXiv preprint arXiv:2106.01178 2021.
430
DFR-Net
78.81 %
90.13 %
60.40 %
0.18 s
1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding:
The devil is in the task: Exploiting reciprocal
appearance-localization features for monocular 3d
object detection
. ICCV 2021.
431
spLBP
78.66 %
81.66 %
61.69 %
1.5 s
8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common
Detection Framework . IEEE Trans. Intelligent Transportation Systems 2016.
432
3D-SSMFCNN
code
78.19 %
77.92 %
69.19 %
0.1 s
GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous
Driving . 2017.
433
MonoGRNet
code
77.94 %
88.65 %
63.31 %
0.04s
NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network
for 3D Object Localization . The Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI-19) 2019.
434
Aug3D-RPN
77.88 %
85.57 %
61.16 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth . 2021.
435
AutoShape
code
77.66 %
86.51 %
64.40 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection . Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
436
Reinspect
code
77.48 %
90.27 %
66.73 %
2s
1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes . CVPR 2016.
437
SARM3D
77.41 %
90.20 %
68.24 %
0.03 s
GPU @ 2.5 Ghz (Python)
438
multi-task CNN
77.18 %
86.12 %
68.09 %
25.1 ms
GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes . IEEE Intelligent Transportation Systems Conference 2018.
439
Regionlets
76.99 %
88.75 %
60.49 %
1 s
>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object
Detection . T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense
Neural Patterns and Regionlets . British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location
Relaxation and Regionlets Relocalization . Asian Conference on Computer
Vision 2014.
440
3DVP
code
76.98 %
84.95 %
65.78 %
40 s
8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns
for Object Category Recognition . IEEE Conference on Computer
Vision and Pattern Recognition 2015.
441
Mobile Stereo R-CNN
76.73 %
90.08 %
62.23 %
1.8 s
NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R-
CNN on Nvidia Jetson TX2 . International Conference on Advanced
Engineering, Technology and Applications
(ICAETA) 2021.
442
SubCat
code
76.36 %
84.10 %
60.56 %
0.7 s
6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by
Clustering
Appearance Patterns . T-ITS 2015.
443
GS3D
76.35 %
86.23 %
62.67 %
2 s
1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection
Framework for Autonomous Driving . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
444
AOG
code
76.24 %
86.08 %
61.51 %
3 s
4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent
Context and Occlusion for Car
Detection and Viewpoint Estimation . TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion
for Car Detection by Hierarchical And-Or Model . ECCV 2014.
445
Pose-RCNN
75.83 %
89.59 %
64.06 %
2 s
>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and
pose estimation using 3D object proposals . Intelligent Transportation Systems
(ITSC), 2016 IEEE 19th International Conference
on 2016.
446
3D FCN
74.65 %
86.74 %
67.85 %
>5 s
1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle
Detection
in Point Cloud . IROS 2017.
447
OC Stereo
code
74.60 %
87.39 %
62.56 %
0.35 s
1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D
Object Detection . ICRA 2020.
448
MAOLoss
code
73.79 %
89.31 %
63.59 %
0.05 s
1 core @ 2.5 Ghz (Python)
449
Kinematic3D
code
71.73 %
89.67 %
54.97 %
0.12 s
1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in
Monocular Video . ECCV 2020 .
450
AOG-View
71.26 %
85.01 %
55.73 %
3 s
1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for
Car Detection by Hierarchical And-Or Model . ECCV 2014.
451
GAC3D
70.73 %
83.30 %
52.23 %
0.25 s
1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D
object detection with
ground-guide model and adaptive convolution . 2021.
452
MV-RGBD-RF
70.70 %
77.89 %
57.41 %
4 s
4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts. . IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection . IEEE Intelligent Vehicles Symposium (IV) 2015.
453
Vote3Deep
70.30 %
78.95 %
63.12 %
1.5 s
4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point
Clouds Using Efficient Convolutional Neural Networks . ArXiv e-prints 2016.
454
ROI-10D
70.16 %
76.56 %
61.15 %
0.2 s
GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape . Computer Vision and Pattern Recognition (CVPR) 2019.
455
RetinaMono
69.83 %
74.54 %
60.95 %
0.02 s
1 core @ 2.5 Ghz (Python)
456
BirdNet+ (legacy)
code
68.05 %
92.10 %
65.61 %
0.1 s
Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
457
Decoupled-3D
67.92 %
87.78 %
54.53 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled
Structured Polygon Estimation and Height-Guided Depth
Estimation . AAAI 2020.
458
SparVox3D
67.88 %
83.76 %
52.56 %
0.05 s
GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance
on Monocular 3D Object Detection Using Bin-Mixing
and Sparse Voxel Data . 2021 6th International
Conference on Computer Science and Engineering
(UBMK) 2021.
459
Pseudo-Lidar
code
67.79 %
85.40 %
58.50 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation:
Bridging the Gap in 3D Object Detection for Autonomous
Driving . The IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
460
OC-DPM
67.06 %
79.07 %
52.61 %
10 s
8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection . IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
461
DPM-VOC+VP
66.72 %
82.15 %
49.01 %
8 s
1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part
Models . IEEE Transactions on Pattern
Analysis and Machine Intelligence (TPAMI) 2015.
462
BdCost48LDCF
code
66.63 %
81.38 %
52.20 %
0.5 s
8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
463
MM
65.65 %
88.80 %
56.53 %
1 s
1 core @ 2.5 Ghz (C/C++)
464
RefinedMPL
65.24 %
88.29 %
53.20 %
0.15 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR
for 3D Object Detection in Autonomous Driving . arXiv preprint arXiv:1911.09712 2019.
465
MDPM-un-BB
64.06 %
79.74 %
49.07 %
60 s
4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based
Models . PAMI 2010.
466
TLNet (Stereo)
code
63.53 %
76.92 %
54.58 %
0.1 s
1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from
Monocular to Stereo 3D Object Detection . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
467
PDV-Subcat
63.24 %
78.27 %
47.67 %
7 s
1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood
differential
statistic feature for pedestrian and face
detection . Pattern Recognition 2017.
468
MDSNet
62.74 %
85.94 %
50.27 %
0.07 s
1 core @ 2.5 Ghz (Python)
469
MODet
62.54 %
66.06 %
60.04 %
0.05 s
GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object
Detection Based on Bird's Eye View on 3D Point
Clouds . 2019 International Conference on
3D Vision (3DV) 2019.
470
Graph-NMS
61.93 %
78.55 %
53.72 %
36 ms
GPU @ 2.5 Ghz (Python)
471
VMDet_Boost
61.54 %
79.36 %
53.56 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
472
SubCat48LDCF
code
61.16 %
78.86 %
44.69 %
0.5 s
8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
473
DPM-C8B1
60.21 %
75.24 %
44.73 %
15 s
4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes . Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM . IV 2014.
474
SAMME48LDCF
code
58.38 %
77.47 %
44.43 %
0.5 s
8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
475
LSVM-MDPM-sv
58.36 %
71.11 %
43.22 %
10 s
4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models . PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout . NIPS 2011.
476
BirdNet
57.12 %
79.30 %
55.16 %
0.11 s
Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework
from LiDAR Information . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
477
ACF-SC
56.60 %
69.90 %
43.61 %
<0.3 s
1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding
System using Context-Aware Object Detection . Robotics and Automation (ICRA),
2015 IEEE International Conference on 2015.
478
LSVM-MDPM-us
code
55.95 %
68.94 %
41.45 %
10 s
4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models . PAMI 2010.
479
Anonymous
54.16 %
71.33 %
47.50 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
480
ACF
54.09 %
63.05 %
41.81 %
0.2 s
1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object
Detection . PAMI 2014. P. Doll\'ar: Piotr's Image and Video
Matlab Toolbox (PMT) . .
481
Mono3D_PLiDAR
code
53.36 %
80.85 %
44.80 %
0.1 s
NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with
Pseudo-LiDAR Point Cloud . arXiv:1903.09847 2019.
482
Graph-NMS-baseline
52.92 %
76.21 %
43.38 %
47 ms
GPU @ 2.5 Ghz (Python)
483
RT3D-GMP
51.95 %
62.41 %
39.14 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
484
VeloFCN
51.82 %
70.53 %
45.70 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network . RSS 2016 .
485
Vote3D
45.94 %
54.38 %
40.48 %
0.5 s
4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object
Detection . Proceedings of Robotics: Science and
Systems 2015.
486
TopNet-HighRes
45.85 %
58.04 %
41.11 %
101ms
NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in
Occupancy Grid Maps Using Deep Convolutional
Networks . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
487
RT3DStereo
45.81 %
56.53 %
37.63 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information . Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
488
Multimodal Detection
code
45.46 %
63.91 %
37.25 %
0.06 s
GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D-
LIDAR and color camera data . Pattern Recognition Letters 2017.
489
CDTrack3D
code
44.58 %
53.11 %
37.41 %
0.0106 s
NVIDIA RTX 3090 GPU, i9 10850k CPU
490
RT3D
39.69 %
50.33 %
40.04 %
0.09 s
GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in
LiDAR Point Cloud for Autonomous Driving . IEEE Robotics and Automation Letters 2018.
491
VoxelJones
code
36.31 %
43.89 %
34.16 %
.18 s
1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures . arXiv preprint arXiv:1907.11306 2019.
492
CSoR
code
21.66 %
31.52 %
17.99 %
3.5 s
4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks
für räumliche Detektion und Klassifikation von
Objekten in Fahrzeugumgebung . 2015.
493
mBoW
21.59 %
35.22 %
16.89 %
10 s
1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words . Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
494
DepthCN
code
21.18 %
37.45 %
16.08 %
2.3 s
GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D-
LIDAR and convnet . IEEE ITSC 2017.
495
YOLOv2
code
14.31 %
26.74 %
10.94 %
0.02 s
GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time
object detection . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger . Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition 2017.
496
TopNet-UncEst
6.24 %
7.24 %
5.42 %
0.09 s
NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing
Object Detection Uncertainty in Multi-Layer Grid
Maps . 2019.
497
MonoDET
code
5.80 %
6.41 %
5.13 %
0.04 s
1 core @ 2.5 Ghz (Python)
498
TopNet-Retina
5.00 %
6.82 %
4.52 %
52ms
GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in
Occupancy Grid Maps Using Deep Convolutional
Networks . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
499
test
code
4.63 %
1.85 %
4.96 %
50 s
1 core @ 2.5 Ghz (Python)
500
TopNet-DecayRate
0.01 %
0.00 %
0.01 %
92 ms
NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in
Occupancy Grid Maps Using Deep Convolutional
Networks . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
501
LaserNet
0.00 %
0.00 %
0.00 %
12 ms
GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object
Detector for Autonomous Driving . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition (CVPR) 2019.
502
Neighbor-Vote
0.00 %
0.00 %
0.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D
Object Detection through Neighbor Distance Voting . ACM MM 2021.