Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
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.
2
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.
3
SA-SSD
95.16 %
97.92 %
90.15 %
0.04 s
1 core @ 2.5 Ghz (Python)
4
3DSSD
95.10 %
97.69 %
92.18 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
5
THU CV-AI
95.04 %
95.29 %
87.73 %
0.38 s
GPU @ 2.5 Ghz (Python)
6
MVRA + I-FRCNN+
94.98 %
95.87 %
82.52 %
0.18 s
GPU @ 2.5 Ghz (Python)
7
PV-RCNN
94.70 %
98.17 %
92.04 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
8
BM-NET
94.49 %
95.09 %
85.06 %
0.5 s
GPU @ 2.5 Ghz (Python + C/C++)
9
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.
10
EPNet
94.44 %
96.15 %
89.99 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
11
CPRCCNN
94.42 %
96.33 %
89.96 %
0.1 s
1 core @ 2.5 Ghz (Python)
12
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.
13
VCTNet
93.97 %
94.48 %
84.23 %
0.02 s
GPU @ 1.5 Ghz (C/C++)
14
DGIST-CellBox
93.90 %
95.86 %
88.26 %
0.1 s
GPU @ 2.5 Ghz (Java + C/C++)
15
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.
16
EM-FPS
93.70 %
94.73 %
86.38 %
0.15 s
GPU @ 1.5 Ghz (Python + C/C++)
17
MDC
93.68 %
96.72 %
83.76 %
0.17 s
GPU @ 2.5 Ghz (Python)
18
THICV-YDM
93.60 %
96.26 %
81.08 %
0.06 s
GPU @ 2.5 Ghz (Python)
19
HRI-SH
93.57 %
96.23 %
86.33 %
3.6 s
GPU @ >3.5 Ghz (Python + C/C++)
20
MLF_PointCas
93.55 %
96.69 %
86.16 %
0.1 s
GPU @ 2.5 Ghz (Python)
21
MonoPair
93.55 %
96.61 %
83.55 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
22
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.
23
Point-GNN
93.50 %
96.58 %
88.35 %
0.6 s
GPU @ 2.5 Ghz (Python)
24
Noah CV Lab - SSL
93.49 %
94.11 %
85.93 %
0.1 s
GPU @ 2.5 Ghz (Python)
25
FichaDL
93.46 %
96.00 %
84.39 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
26
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.
27
Alibaba-AILabsX
93.35 %
96.38 %
85.92 %
0.2 s
GPU @ >3.5 Ghz (Python)
28
ORP
93.27 %
96.76 %
85.86 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
29
CFENet
93.26 %
93.91 %
86.99 %
4 s
GPU @ 2.5 Ghz (Python + C/C++)
30
STD
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.
31
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.
32
Fast Point R-CNN v1
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.
33
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.
34
ELE
93.14 %
98.44 %
90.32 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
35
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.
36
RGB3D
93.07 %
96.54 %
88.04 %
0.39 s
GPU @ 2.5 Ghz (Python)
37
DH-ARI
93.01 %
94.55 %
87.84 %
0.2 s
1 core @ >3.5 Ghz (Python + C/C++)
38
PointRCNN-deprecated
92.96 %
96.72 %
85.81 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
39
Fast Point R-CNN
92.93 %
96.02 %
87.41 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
40
SerialR-FCN+SG-NMS
92.93 %
95.72 %
82.92 %
0.2 s
1 core @ 2.5 Ghz (Python)
41
DH-ARI
92.83 %
92.75 %
82.92 %
4s
GPU @ 2.5 Ghz (C/C++)
42
SegVoxelNet
92.73 %
96.00 %
87.60 %
0.04 s
1 core @ 2.5 Ghz (Python)
43
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.
44
PPFNet
code
92.68 %
96.32 %
87.66 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
45
R-GCN
92.67 %
96.19 %
87.66 %
0.16 s
GPU @ 2.5 Ghz (Python)
46
MVX-Net
92.66 %
96.06 %
85.33 %
0.06 s
GPU @ 3.0 Ghz (Python + C/C++)
47
PI-RCNN
92.66 %
96.17 %
87.68 %
0.1 s
1 core @ 2.5 Ghz (Python)
48
OHS + Occ
92.65 %
96.09 %
89.72 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
49
NU-optim
92.63 %
95.67 %
87.37 %
0.04 s
GPU @ >3.5 Ghz (Python)
50
3D-CVF
92.60 %
96.20 %
89.60 %
0.05 s
GPU @ >3.5 Ghz (Python)
51
ART-Det
92.59 %
97.82 %
81.89 %
0.067s
GPU @ 2.5 Ghz (Python + C/C++)
52
deprecated
92.59 %
96.21 %
89.58 %
0.05 s
GPU @ >3.5 Ghz (Python)
53
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 . arXiv preprint arXiv:1911.10150 2019.
54
SPA
92.56 %
95.96 %
87.60 %
0.1 s
1 core @ 2.5 Ghz (Python)
55
DEFT
92.55 %
96.17 %
89.51 %
1 s
GPU @ 2.5 Ghz (Python)
56
CONV-BOX
92.53 %
95.76 %
87.60 %
0.2 s
Tesla V100
57
Associate-3Ddet
92.45 %
95.61 %
87.32 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
58
IPOD
92.44 %
95.54 %
87.55 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector
for Point Cloud . CoRR 2018.
59
CP
92.44 %
96.14 %
87.58 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
60
YOLOv3.5
92.42 %
95.22 %
82.32 %
0.05 s
GPU @ 2.5 Ghz (Python)
61
OHS
92.39 %
95.84 %
89.51 %
0.03 s
1 core @ 2.5 Ghz (Python/C++)
62
PointRGCN
92.33 %
97.51 %
87.07 %
0.26 s
GPU @ V100 (Python)
63
F-ConvNet
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.
64
IE-PointRCNN
92.08 %
96.01 %
87.05 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
65
ECV-NET
92.07 %
94.49 %
84.25 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
66
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.
67
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.
68
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.
69
MMLab-PartA^2
91.86 %
95.03 %
89.06 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and
Aggregation Neural Network for Object Detection
from Point Cloud . arXiv preprint arXiv:1907.03670 2019.
70
MBR-SSD
91.83 %
93.46 %
84.97 %
4.0 s
GPU @ 2.5 Ghz (Python + C/C++)
71
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.
72
MLF_SecCas
91.76 %
96.53 %
83.90 %
0.05 s
1 core @ 2.5 Ghz (Python)
73
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.
74
HRI-FusionRCNN
91.70 %
94.61 %
84.10 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
75
PiP
91.67 %
94.35 %
88.35 %
0.05 s
1 core @ 2.5 Ghz (Python)
76
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.
77
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.
78
deprecated
91.59 %
94.34 %
79.14 %
0.05 s
GPU @ 2.0 Ghz (Python)
79
Det-RGBD
91.49 %
94.30 %
79.41 %
0.58 s
GPU @ 2.5 Ghz (Python + C/C++)
80
HRI-VoxelFPN
91.44 %
96.65 %
86.18 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds . arXiv preprint arXiv:1907.05286v2 2019.
81
TBA
91.43 %
93.99 %
88.51 %
0.07 s
1 core @ 2.5 Ghz (Python)
82
RUC
91.40 %
95.02 %
88.41 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
83
FNV2
91.39 %
96.20 %
81.33 %
0.18 s
GPU @ 2.5 Ghz (Python)
84
Faster RCNN + G
91.28 %
94.34 %
81.02 %
0.19 s
GPU @ 2.5 Ghz (Python)
85
PFPN
91.25 %
94.33 %
81.41 %
0.02 s
4 cores @ >3.5 Ghz (Python)
86
Alibaba-AILabsX
91.23 %
96.33 %
83.75 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
87
OACV
91.21 %
94.23 %
83.07 %
0.23 s
GPU @ 2.5 Ghz (Python)
88
CentrNet-v1
91.21 %
94.22 %
88.36 %
0.03 s
GPU @ 2.5 Ghz (Python)
89
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.
90
Faster RCNN
91.19 %
94.43 %
80.99 %
0.2 s
GPU @ 2.5 Ghz (Python)
91
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.
92
PointPiallars_SECA
91.12 %
93.66 %
87.94 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
93
DDB
91.12 %
93.71 %
87.34 %
0.05 s
GPU @ 2.5 Ghz (Python)
94
AILabs3D
91.11 %
96.38 %
85.77 %
0.6 s
GPU @ >3.5 Ghz (Python)
95
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.
96
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.
97
PCSC-Net
90.97 %
94.20 %
87.38 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
98
MMV
90.91 %
94.16 %
83.36 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
99
A-VoxelNet
90.86 %
93.84 %
83.27 %
0.029 s
GPU @ 2.5 Ghz (Python)
100
VAT-Net
90.83 %
96.07 %
80.56 %
1 s
GPU @ 2.5 Ghz (Python + C/C++)
101
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.
102
MVSLN
90.81 %
96.12 %
83.39 %
0.1s s
1 core @ 2.5 Ghz (C/C++)
103
MPNet
90.80 %
94.68 %
87.30 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
104
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.
105
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.
106
Tencent_ADlab_Lidar
90.74 %
93.80 %
86.75 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
107
SRF
90.69 %
95.88 %
85.52 %
0.05 s
GPU @ 2.5 Ghz (Python + C/C++)
108
HR-SECOND
code
90.68 %
93.72 %
85.63 %
0.11 s
1 core @ 2.5 Ghz (Python + C/C++)
109
GA2500
90.68 %
95.86 %
80.29 %
0.2 s
1 core @ 2.5 Ghz (Python)
110
GA_rpn500
90.68 %
95.86 %
80.29 %
1 s
1 core @ 2.5 Ghz (Python)
111
TANet
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.
112
SFB-SECOND
90.67 %
96.17 %
85.43 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
113
SECOND-V1.5
code
90.65 %
95.96 %
85.35 %
0.04 s
GPU @ 2.0 Ghz (Python + C/C++)
114
PTS
code
90.64 %
95.74 %
85.41 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
115
VOXEL_FPN_HR
90.55 %
93.76 %
85.42 %
0.12 s
8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
116
FOFNet
90.52 %
94.00 %
85.20 %
0.04 s
GPU @ 2.5 Ghz (Python)
117
MP
90.50 %
93.86 %
85.17 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
118
Sogo_MM
90.46 %
94.31 %
80.62 %
1.5 s
GPU @ 2.5 Ghz (C/C++)
119
bigger_ga
90.38 %
95.76 %
77.92 %
1 s
1 core @ 2.5 Ghz (Python)
120
RCN-resnet101
90.35 %
92.36 %
80.58 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
121
AtrousDet
90.35 %
95.94 %
77.94 %
0.05 s
TITAN X
122
InNet
90.30 %
95.42 %
82.05 %
0.16 s
GPU @ 3.5 Ghz (Python + C/C++)
123
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.
124
SECOND
code
90.21 %
93.79 %
82.94 %
38 ms
1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional
Detection . Sensors 2018.
125
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.
126
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.
127
BVVF
90.15 %
95.65 %
84.95 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
128
SAANet
90.14 %
95.93 %
82.95 %
0.10 s
1 core @ 2.5 Ghz (Python)
129
SAG-Net
90.08 %
94.77 %
80.27 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
130
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.
131
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.
132
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.
133
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 . arXiv preprint arXiv:1811.06666 2018.
134
ZRNet(ResNet-50)
89.92 %
95.24 %
79.69 %
0.04 s
GPU @ 2.5 Ghz (Python)
135
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.
136
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.
137
DFD
89.72 %
93.37 %
82.41 %
0.05 s
GPU @ 2.0 Ghz (Python + C/C++)
138
ZRNet
89.72 %
93.97 %
79.47 %
0.04 s
GPU @ 2.5 Ghz (Python)
139
PP_v1.0
code
89.71 %
93.42 %
86.12 %
0.02s
1 core @ 2.5 Ghz (C/C++)
140
SeoulRobotics-HFD
89.68 %
93.30 %
84.52 %
0.035 s
GPU (C++)
141
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.
142
PAD
89.49 %
93.43 %
85.85 %
0.15 s
1 core @ 2.5 Ghz (Python)
143
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.
144
4D-MSCNN+CRL
89.37 %
92.40 %
77.00 %
0.2 s
GPU @ 2.5 Ghz (Matlab + C/C++)
145
MonoDIS
89.15 %
94.61 %
78.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
146
cas+res+soft
89.14 %
94.54 %
78.37 %
0.2 s
4 cores @ 2.5 Ghz (Python)
147
merge12-12
88.96 %
94.58 %
78.22 %
0.2 s
4 cores @ 2.5 Ghz (Python)
148
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.
149
CLA
88.86 %
94.16 %
76.53 %
0.3 s
GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware
Deformable Convolution and Backward Attention
Filtering . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) 2019.
150
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.
151
SS3D_HW
88.68 %
94.49 %
68.79 %
0.4 s
GPU @ 2.5 Ghz (Python)
152
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.
153
DA
88.63 %
94.59 %
76.00 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
154
CRCNNA
88.59 %
94.82 %
76.74 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
155
3DNN
88.56 %
94.52 %
81.51 %
0.09 s
GPU @ 2.5 Ghz (Python)
156
CSFADet
88.54 %
93.75 %
78.62 %
0.05 s
GPU @ 2.5 Ghz (Python)
157
ODES
code
88.53 %
90.28 %
77.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
158
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.
159
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.
160
CFR
88.48 %
94.12 %
80.89 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
161
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.
162
PointRes
88.41 %
95.38 %
84.22 %
0.013 s
1 core @ 2.5 Ghz (Python + C/C++)
163
TridentNet
88.37 %
90.33 %
80.57 %
0.2 s
GPU @ 2.5 Ghz (Python)
164
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.
165
NLK
87.89 %
91.65 %
83.32 %
0.02 s
1 core @ 2.5 Ghz (Python)
166
Multi-3D
87.87 %
93.70 %
76.07 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
167
ELLIOT
87.83 %
93.18 %
84.24 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
168
ga50
87.65 %
95.76 %
75.14 %
1 s
1 core @ 2.5 Ghz (Python)
169
cas_retina
87.64 %
93.87 %
75.30 %
0.2 s
4 cores @ 2.5 Ghz (Python)
170
SMOKE
87.51 %
93.21 %
77.66 %
0.03 s
GPU @ 2.5 Ghz (Python)
171
MonoSS
87.46 %
93.15 %
77.58 %
0.03 s
GPU @ 2.5 Ghz (Python + C/C++)
172
cascadercnn
87.36 %
89.37 %
73.42 %
0.36 s
4 cores @ 2.5 Ghz (Python)
173
SCANet
87.28 %
92.91 %
81.99 %
0.17 s
>8 cores @ 2.5 Ghz (Python)
174
SECA
87.16 %
94.95 %
80.01 %
0.09 s
GPU @ 2.5 Ghz (Python)
175
SCANet
86.94 %
92.69 %
79.95 %
0.09s
GPU @ 2.5 Ghz (Python)
176
anm
86.52 %
94.88 %
76.46 %
3 s
1 core @ 2.5 Ghz (C/C++)
177
DSGN
86.43 %
95.53 %
78.75 %
0.67 s
NVIDIA Tesla V100
178
ReSqueeze
86.12 %
90.35 %
76.53 %
0.03 s
GPU @ >3.5 Ghz (Python)
179
IoU_DCRCNN
86.07 %
90.04 %
78.14 %
0.66 s
GPU @ 2.5 Ghz (Python)
180
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.
181
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.
182
ResNet-RRC w/RGBD
85.58 %
91.32 %
74.80 %
0.057 s
GPU @ 1.5 Ghz (Python + C/C++)
183
X_MD
85.52 %
93.31 %
78.25 %
0.2 s
1 core @ 2.5 Ghz (Python + C/C++)
184
cas_retina_1_13
85.48 %
91.54 %
74.60 %
0.03 s
4 cores @ 2.5 Ghz (Python)
185
NEUAV
85.42 %
89.67 %
77.28 %
0.06 s
GPU @ 2.5 Ghz (Python)
186
ResNet-RRC
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.
187
Cmerge
85.32 %
93.40 %
70.57 %
0.2 s
4 cores @ 2.5 Ghz (Python)
188
PL V2 (SDN+GDC)
85.15 %
94.95 %
77.78 %
0.6 s
GPU @ 2.5 Ghz (C/C++)
189
RAR-Net
85.08 %
89.04 %
69.26 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
190
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 .
191
FNV1_RPN
85.07 %
94.59 %
79.91 %
0.12 s
1 core @ 2.5 Ghz (Python + C/C++)
192
FNV1_Fusion
85.02 %
92.64 %
79.77 %
0.11 s
GPU @ 2.5 Ghz (Python)
193
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.
194
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.
195
LPN
84.77 %
89.19 %
74.08 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
196
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.
197
SECA
84.60 %
92.51 %
79.53 %
1 s
GPU @ 2.5 Ghz (Python)
198
VSE
84.60 %
92.51 %
79.53 %
0.15 s
GPU @ 2.5 Ghz (Python)
199
PG-MonoNet
84.42 %
88.61 %
68.59 %
0.19 s
GPU @ 2.5 Ghz (Python)
200
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.
201
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.
202
YOLOv3+d
84.09 %
86.66 %
75.08 %
0.04 s
GPU @ 1.5 Ghz (C/C++)
203
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.
204
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 . arXiv preprint arXiv:1912.04799 2019.
205
ASOD
83.52 %
94.09 %
68.68 %
0.28 s
GPU @ 2.5 Ghz (Python)
206
softretina
83.30 %
93.55 %
70.59 %
0.16 s
4 cores @ 2.5 Ghz (Python)
207
FNV1
83.20 %
91.34 %
75.93 %
0.11 s
GPU @ 2.5 Ghz (Python)
208
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.
209
ZKNet
82.96 %
92.17 %
72.43 %
0.01 s
GPU @ 2.0 Ghz (Python)
210
Pseudo-LiDAR V2
code
82.90 %
94.46 %
75.45 %
0.4 s
GPU @ 2.5 Ghz (Python)
211
Retinanet100
82.73 %
93.97 %
68.37 %
0.2 s
4 cores @ 2.5 Ghz (Python)
212
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.
213
Pseudo-LiDAR E2E
82.54 %
94.00 %
75.31 %
0.4 s
GPU @ 2.5 Ghz (Python)
214
Disp R-CNN
82.47 %
93.15 %
70.35 %
0.42 s
GPU @ 2.5 Ghz (Python + C/C++)
215
Disp R-CNN (velo)
82.40 %
93.11 %
70.26 %
0.42 s
GPU @ 2.5 Ghz (Python + C/C++)
216
cascade_gw
82.35 %
85.98 %
71.60 %
0.2 s
4 cores @ 2.5 Ghz (Python)
217
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.
218
CBNet
81.70 %
91.47 %
72.02 %
1 s
4 cores @ 2.5 Ghz (Python)
219
detectron
code
81.51 %
91.43 %
69.50 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
220
Resnet101Faster rcnn
81.44 %
91.08 %
71.52 %
1 s
1 core @ 2.5 Ghz (Python)
221
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.
222
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.
223
MTDP
80.97 %
89.03 %
66.91 %
0.15 s
GPU @ 2.0 Ghz (Python)
224
RFCN_RFB
80.89 %
88.07 %
69.66 %
0.2 s
4 cores @ 2.5 Ghz (Python)
225
Manhnet
80.85 %
89.06 %
64.29 %
26 ms
1 core @ 2.5 Ghz (C/C++)
226
centernet
80.78 %
90.29 %
70.53 %
0.01 s
GPU @ 2.5 Ghz (Python)
227
RADNet-Fusion
80.04 %
76.72 %
76.78 %
0.1 s
1 core @ 2.5 Ghz (Python)
228
NM
code
79.98 %
90.71 %
68.98 %
0.01 s
GPU @ 2.5 Ghz (Python)
229
RADNet-LIDAR
79.59 %
75.20 %
76.03 %
0.1 s
1 core @ 2.5 Ghz (Python)
230
MMRetina
79.53 %
89.66 %
69.52 %
0.38 s
GPU @ 2.5 Ghz (Python)
231
SceneNet
79.26 %
90.70 %
67.98 %
0.03 s
GPU @ 2.5 Ghz (C/C++)
232
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.
233
CLF3D
79.05 %
87.57 %
67.58 %
0.13 s
GPU @ 2.5 Ghz (Python)
234
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.
235
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.
236
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.
237
yolov3_warp
77.61 %
92.24 %
65.70 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
238
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.
239
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.
240
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.
241
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.
242
FailNet-Fusion
76.90 %
74.55 %
71.94 %
0.1 s
1 core @ 2.5 Ghz (Python)
243
RTL3D
76.74 %
79.68 %
72.56 %
0.02 s
GPU @ 2.5 Ghz (Python)
244
avodC
76.58 %
87.30 %
71.65 %
0.1 s
GPU @ 2.5 Ghz (Python)
245
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.
246
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.
247
FailNet-LIDAR
76.26 %
74.16 %
71.24 %
0.1 s
1 core @ 2.5 Ghz (Python)
248
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.
249
bin
76.16 %
78.73 %
63.39 %
15ms s
GPU @ >3.5 Ghz (Python)
250
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.
251
VoxelNet(Unofficial)
75.22 %
81.37 %
68.74 %
0.5 s
GPU @ 2.0 Ghz (Python)
252
RFCN
75.14 %
83.04 %
61.55 %
0.2 s
4 cores @ 2.5 Ghz (Python)
253
myfaster-rcnn-v1.5
74.93 %
89.85 %
62.56 %
0.1 s
1 core @ 2.5 Ghz (Python)
254
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.
255
OC Stereo
74.60 %
87.39 %
62.56 %
0.35 s
1 core @ 2.5 Ghz (Python + C/C++)
256
yolo800
74.31 %
78.93 %
63.83 %
0.13 s
4 cores @ 2.5 Ghz (Python)
257
ResNet-RRC (Noised)
74.30 %
79.15 %
64.80 %
.057 s
GPU @ 1.5 Ghz (Python + C/C++)
258
3DVSSD
74.11 %
86.99 %
63.57 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
259
Multi-task DG
74.07 %
91.06 %
64.48 %
0.06 s
GPU @ 2.5 Ghz (Python)
260
FD2
73.93 %
88.65 %
64.62 %
0.01 s
GPU @ >3.5 Ghz (Python + C/C++)
261
BdCost+DA+BB+MS
73.72 %
85.18 %
57.79 %
TBD s
4 cores @ 2.5 Ghz (Matlab + C/C++)
262
m-prcnn
73.64 %
87.64 %
57.03 %
0.43 s
1 core @ 2.5 Ghz (Python)
263
BdCost+DA+MS
73.62 %
85.03 %
58.94 %
TBD s
4 cores @ 2.5 Ghz (Matlab/C++)
264
Int-YOLO
code
73.23 %
75.81 %
63.59 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
265
stereo_sa
72.99 %
87.88 %
63.49 %
0.3 s
GPU @ 2.5 Ghz (Python)
266
RuiRUC
72.08 %
87.48 %
55.28 %
0.12 s
1 core @ 2.5 Ghz (Python)
267
ANM
71.97 %
87.17 %
55.19 %
0.12 s
1 core @ 2.5 Ghz (Python)
268
MF3D
71.85 %
91.50 %
57.46 %
0.03 s
GPU @ 2.5 Ghz (C/C++)
269
RFBnet
71.66 %
87.25 %
63.00 %
0.2 s
4 cores @ 2.5 Ghz (Python)
270
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.
271
GPVL
71.06 %
81.67 %
54.96 %
10 s
1 core @ 2.5 Ghz (C/C++)
272
BdCost+DA+BB
70.86 %
85.52 %
56.19 %
TBD s
4 cores @ 2.5 Ghz (Matlab + C/C++)
273
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.
274
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.
275
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.
276
fasterrcnn
69.45 %
74.76 %
60.20 %
0.2 s
4 cores @ 2.5 Ghz (Python)
277
myfaster-rcnn
68.38 %
90.54 %
55.97 %
0.01 s
1 core @ 2.5 Ghz (Python)
278
Decoupled-3D v2
68.17 %
88.64 %
54.74 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
279
Decoupled-3D
67.92 %
87.78 %
54.53 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
280
Pseudo-LiDAR
code
67.79 %
85.40 %
58.50 %
0.4 s
GPU @ 2.5 Ghz (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 . CVPR 2019.
281
SA_3D
67.50 %
88.90 %
53.04 %
0.3 s
GPU @ 2.5 Ghz (Python)
282
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.
283
mymask-rcnn
66.82 %
88.60 %
52.18 %
0.3 s
1 core @ 2.5 Ghz (Python)
284
Fast-SSD
66.79 %
85.19 %
57.89 %
0.06 s
GTX650Ti
285
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.
286
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.
287
E-VoxelNet
65.33 %
68.00 %
57.84 %
0.1 s
GPU @ 2.5 Ghz (Python)
288
RefinedMPL
65.24 %
88.29 %
53.20 %
0.1 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.
289
BdCost48-25C
64.63 %
81.42 %
52.22 %
4 s
1 core @ 2.5 Ghz (C/C++)
290
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.
291
64.06 %
81.75 %
54.83 %
292
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.
293
MODet
62.54 %
66.06 %
60.04 %
0.05 s
GTX1080Ti
294
yl_net
61.78 %
66.00 %
60.36 %
0.03 s
GPU @ 2.5 Ghz (Python)
295
Lidar_ROI+Yolo(UJS)
61.71 %
73.32 %
53.65 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
296
GNN
61.48 %
79.09 %
51.06 %
0.2 s
1 core @ 2.5 Ghz (Python)
297
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.
298
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.
299
tiny_rfdet
code
59.94 %
65.51 %
57.20 %
0.01 s
GPU @ 2.5 Ghz (Python)
300
RADNet-Mono
59.85 %
67.47 %
54.14 %
0.1 s
1 core @ 2.5 Ghz (Python)
301
monoref3d
58.97 %
78.11 %
47.72 %
0.1 s
1 core @ 2.5 Ghz (Python)
302
ref3D
58.97 %
78.11 %
47.72 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
303
100Frcnn
58.92 %
82.09 %
49.04 %
2 s
4 cores @ 2.5 Ghz (Python + C/C++)
304
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.
305
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.
306
ref3D
57.16 %
77.96 %
45.99 %
0.1 s
1 core @ 2.5 Ghz (Python)
307
BirdNet
57.02 %
78.91 %
55.08 %
0.11 s
Titan Xp GPU
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.
308
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.
309
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.
310
mylsi-faster-rcnn
55.81 %
80.45 %
47.38 %
0.3 s
1 core @ 2.5 Ghz (Python)
311
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) . .
312
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.
313
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 .
314
FailNet-Mono
47.95 %
59.59 %
41.33 %
0.1 s
1 core @ 2.5 Ghz (Python)
315
DLMB
46.50 %
60.92 %
41.59 %
0.03 s
8 cores @ 3.5 Ghz (C/C++)
316
softyolo
45.97 %
66.08 %
38.02 %
0.16 s
4 cores @ 2.5 Ghz (Python)
317
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.
318
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.
319
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.
320
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.
321
rpn
40.80 %
67.42 %
32.16 %
0.01 s
1 core @ 2.5 Ghz (Python)
322
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.
323
FCY
36.35 %
42.46 %
36.09 %
0.02 s
GPU @ 2.5 Ghz (Python)
324
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.
325
Licar
35.19 %
42.34 %
33.97 %
0.09 s
GPU @ 2.0 Ghz (Python)
326
KD53-20
34.76 %
51.76 %
29.39 %
0.19 s
4 cores @ 2.5 Ghz (Python)
327
DT3D
34.07 %
50.81 %
31.46 %
0,21s
GPU @ 2.5 Ghz (Python)
328
SAIC-SA-3D
31.16 %
41.51 %
29.83 %
0.05 s
GPU @ 2.5 Ghz (Python)
329
FCN-Depth
code
25.05 %
52.32 %
18.07 %
1 s
GPU @ 1.5 Ghz (Matlab + C/C++)
330
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.
331
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.
332
R-CNN_VGG
21.36 %
29.38 %
16.61 %
10 s
GPU @ 2.5 Ghz (Matlab + C/C++)
333
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.
334
DLnet
15.90 %
21.22 %
13.78 %
0.3 s
4 cores @ 2.5 Ghz (C/C++)
335
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.
336
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.
337
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.
338
FCPP
0.07 %
0.01 %
0.07 %
0.02 s
1 core @ 2.0 Ghz (Python + C/C++)
339
ANM
0.01 %
0.01 %
0.02 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
340
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.
341
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.
342
SN-net
0.00 %
0.00 %
0.00 %
0.8 s
GPU @ 2.5 Ghz (Python + C/C++)
343
JSyolo
0.00 %
0.00 %
0.00 %
0.16 s
4 cores @ 2.5 Ghz (Python)