Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
ZEEWAIN-AI
96.14 %
95.22 %
88.94 %
0.3 s
GPU @ 2.5 Ghz (Python)
2
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.
3
EA-M-RCNN(BorderAtt)
95.88 %
96.68 %
90.89 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
4
PVGNet
95.80 %
96.87 %
93.05 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
5
ADLAB
95.69 %
96.69 %
90.81 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
6
SE-SSD
95.60 %
96.69 %
90.53 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
7
HUAWEI Octopus
95.50 %
96.30 %
92.81 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
8
SPANet
95.46 %
96.54 %
90.47 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
9
PLNL-3DSSD
95.38 %
96.37 %
90.31 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
10
CityBrainLab-TSD
95.21 %
96.18 %
90.48 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
11
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.
12
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.
13
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.
14
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
. arXiv preprint arXiv:2012.15712 2020.
15
TBD
95.10 %
96.48 %
92.62 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
16
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.
17
3DIoU++
95.06 %
96.37 %
90.52 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
18
PV-RCNN-v2
95.05 %
96.08 %
92.42 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
19
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.
20
SIENet
94.90 %
95.98 %
92.39 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
21
DomainAdp+PVRCNN
94.85 %
95.99 %
92.27 %
0.09 s
GPU @ 2.5 Ghz (Python)
22
E^2-PV-RCNN
94.80 %
95.95 %
92.26 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
23
XView
94.77 %
95.89 %
92.23 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
24
MSG-PGNN
94.75 %
95.86 %
92.16 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
25
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.
26
3DIoU_v2
94.70 %
96.15 %
92.37 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
27
CVRS VIC-Net
94.69 %
95.79 %
91.89 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
28
HyBrid Feature Det
94.69 %
95.89 %
92.11 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
29
PC-RGNN
94.68 %
95.80 %
92.20 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
30
D3D
94.66 %
95.43 %
89.72 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
31
LZY_RCNN
94.65 %
95.81 %
92.08 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
32
DSA-PV-RCNN
94.64 %
95.86 %
92.10 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
33
FSA-PVRCNN
94.63 %
95.81 %
92.06 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
34
Fast VP-RCNN
code
94.62 %
98.00 %
91.91 %
0.05 s
1 core @ 3.5 Ghz (C/C++)
35
nonet
94.62 %
95.86 %
91.86 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
36
RangeIoUDet
94.61 %
95.74 %
91.98 %
0.02 s
1 core @ 2.5 Ghz (Python)
37
MSL3D
94.60 %
95.76 %
92.16 %
0.03 s
GPU @ 2.5 Ghz (Python)
38
Multi-Sensor3D
94.60 %
95.76 %
92.16 %
0.03 s
GPU @ 2.5 Ghz (Python)
39
CN
94.60 %
97.86 %
89.81 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
40
ReFineNet
94.59 %
95.75 %
92.12 %
0.08 s
1 core @ 2.5 Ghz (Python)
41
MGACNet
94.57 %
95.35 %
91.77 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
42
anonymous
code
94.53 %
97.51 %
91.80 %
0.05s
1 core @ >3.5 Ghz (python)
43
FPC3D
94.52 %
96.06 %
91.72 %
33 s
1 core @ 2.5 Ghz (C/C++)
44
FPC-RCNN
94.51 %
96.15 %
91.80 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
45
RangeRCNN-LV
94.51 %
95.93 %
92.07 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
46
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.
47
PF-GAP
94.47 %
96.13 %
90.15 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
48
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.
49
GNN-RCNN
94.44 %
95.85 %
91.96 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
50
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.
51
CVRS VIC-RCNN
94.38 %
95.89 %
91.90 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
52
CVRS_PF
94.37 %
95.56 %
91.43 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
53
CVIS-DF3D_v2
94.33 %
95.70 %
91.72 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
54
SVGA-Net
94.28 %
95.69 %
91.73 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
55
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.
56
SRDL
94.24 %
95.86 %
91.80 %
0.15 s
GPU @ 2.5 Ghz (Python + C/C++)
57
Baseline of CA RCNN
94.23 %
95.84 %
91.80 %
0.1 s
GPU @ 2.5 Ghz (Python)
58
CVIS-DF3D
94.23 %
95.84 %
91.80 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
59
tbd
code
94.21 %
95.68 %
91.49 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
60
TBD
94.21 %
95.51 %
91.69 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
61
GAP-soft-filter
94.20 %
95.81 %
91.53 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
62
HR-faster-rcnn
94.14 %
95.41 %
86.88 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
63
FPCR-CNN
94.13 %
95.95 %
91.20 %
0.05 s
1 core @ 2.5 Ghz (Python)
64
SAA-PV-RCNN
94.11 %
95.01 %
92.50 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
65
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.
66
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 . arXiv preprint arXiv:2011.01404 2020.
67
SIF
93.95 %
95.51 %
91.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
68
OAP
93.93 %
96.85 %
86.37 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
69
HRI-MSP-L
93.92 %
95.51 %
91.42 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
70
AF_V1
93.87 %
94.45 %
86.37 %
0.1 s
1 core @ 2.5 Ghz (Python)
71
Associate-3Ddet_v2
93.77 %
96.83 %
88.57 %
0.04 s
1 core @ 2.5 Ghz (Python)
72
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.
73
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.
74
VAL
93.71 %
96.92 %
83.76 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
75
XView-PartA^2
93.71 %
95.42 %
91.26 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
76
HIKVISION-ADLab-HZ
93.69 %
96.70 %
88.66 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
77
deprecated
93.68 %
96.92 %
86.15 %
deprecated
deprecated
78
modat3D
93.66 %
94.26 %
83.63 %
0.03 s
GPU @ 2.5 Ghz (Python)
79
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.
80
TBD
93.64 %
95.31 %
91.21 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
81
CBi-GNN
93.60 %
98.89 %
88.47 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
82
MVX-Net++
93.58 %
96.41 %
88.51 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
83
AM-SSD
93.58 %
96.78 %
90.61 %
0.04 s
1 core @ 2.5 Ghz (Python)
84
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.
85
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.
86
CM3DV
93.53 %
96.79 %
88.35 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
87
CIA-SSD v2
93.52 %
96.63 %
88.21 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
88
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.
89
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.
90
PP-3D
93.50 %
96.58 %
88.35 %
0.1 s
1 core @ 2.5 Ghz (Python)
91
FCY
93.49 %
96.74 %
88.39 %
0.02 s
GPU @ 2.5 Ghz (Python)
92
Seg-RCNN
code
93.49 %
96.74 %
88.10 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
93
CJJ
93.48 %
96.68 %
90.63 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
94
AIMC-RUC
93.47 %
96.75 %
88.35 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
95
PointRes
93.47 %
96.69 %
90.46 %
0.013 s
1 core @ 2.5 Ghz (Python + C/C++)
96
dgist_multiDetNet
93.46 %
94.99 %
85.46 %
0.08 s
GPU Titanx Pascal (Python)
97
CDE-Net(0.3)
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: Multi sensor fusion 3d object detection method . Submitted to OSA 2021.
98
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.
99
Cas-SSD
93.41 %
96.73 %
88.30 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
100
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.
101
DGIST MT-CNN
93.39 %
95.16 %
85.50 %
0.09 s
GPU @ 1.0 Ghz (Python)
102
KNN-GCNN
93.39 %
96.19 %
88.17 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
103
F-3DNet
93.38 %
96.51 %
88.32 %
0.5 s
GPU @ 2.5 Ghz (Python)
104
HR-Cascade-RCNN
93.37 %
95.74 %
87.44 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
105
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.
106
PSS
93.36 %
96.64 %
90.52 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
107
FLID
93.35 %
95.90 %
85.69 %
0.04 s
GPU @ 2.5 Ghz (Python)
108
ISF-v2
93.34 %
96.73 %
90.54 %
0.04 s
1 core @ 2.5 Ghz (Python)
109
CDE-Net(0.4)
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: Multi sensor fusion 3d object detection method . Submitted to OSA 2021.
110
RoIFusion
code
93.30 %
96.30 %
88.22 %
0.22 s
1 core @ 3.0 Ghz (Python)
111
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.
112
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.
113
H^23D R-CNN
93.20 %
96.20 %
90.55 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
114
TBD
93.18 %
95.73 %
90.88 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
115
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.
116
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.
117
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.
118
BLPNet_V2
93.11 %
96.07 %
88.06 %
0.04 s
1 core @ 2.5 Ghz (Python)
119
PVF-NET
93.08 %
96.03 %
88.04 %
0.1 s
1 core @ 2.5 Ghz (Python)
120
3DIoU+++
93.06 %
96.08 %
90.53 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
121
HV
93.04 %
95.91 %
87.88 %
0.02 s
GPU @ 2.5 Ghz (Python)
122
NLK-ALL
code
92.98 %
95.73 %
88.13 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
123
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.
124
FPGNN
92.83 %
96.26 %
87.69 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
125
TBD
92.82 %
96.06 %
88.00 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
126
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.
127
deprecated
92.79 %
95.56 %
91.62 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
128
IGRP
92.78 %
96.28 %
87.81 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
129
DPointNet
92.77 %
95.55 %
89.63 %
0.07s
1 core @ 2.5 Ghz (C/C++)
130
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.
131
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.
132
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.
133
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.
134
NLK-3D
92.67 %
95.44 %
87.72 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
135
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.
136
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.
137
SIEV-Net
92.56 %
95.56 %
87.40 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
138
DASS
92.53 %
96.23 %
87.75 %
0.09 s
1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic
Segmentation by Learning 3D Object Detection . 2020.
139
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.
140
VAR
92.46 %
95.11 %
89.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
141
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.
142
Dccnet
92.34 %
96.00 %
86.85 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
143
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.
144
CCFNET
92.25 %
95.85 %
89.36 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
145
LSNet
92.23 %
96.06 %
87.35 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
146
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.
147
MDA
92.17 %
94.88 %
89.54 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
148
PFF3D
92.15 %
95.37 %
87.54 %
0.05 s
GPU @ 3.0 Ghz (Python + C/C++)
149
yolo4
92.13 %
94.20 %
79.89 %
0.02 s
1 core @ 2.5 Ghz (Python)
150
TBD
92.12 %
93.48 %
89.56 %
0.05 s
GPU @ 2.5 Ghz (Python)
151
PVNet
92.12 %
94.84 %
89.27 %
0,1 s
1 core @ 2.5 Ghz (Python)
152
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.
153
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.
154
VGCN
91.97 %
94.91 %
89.34 %
0.09 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
155
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.
156
MKFFNet
91.88 %
95.29 %
89.21 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
157
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.
158
Pointpillar_TV
91.82 %
94.82 %
88.57 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
159
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.
160
3DBN_2
91.75 %
95.34 %
89.12 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
161
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.
162
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.
163
yolo4_5l
91.71 %
93.35 %
79.49 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
164
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.
165
VOXEL_3D
91.61 %
94.50 %
86.37 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
166
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.
167
tt
code
91.59 %
95.15 %
88.72 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
168
MKFFNet
91.54 %
95.32 %
89.02 %
0.01s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
169
V3D
91.52 %
94.46 %
86.34 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
170
MKFFNet
91.51 %
95.19 %
89.01 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
171
GA-Aug
91.46 %
94.55 %
84.85 %
0.04 s
GPU @ 2.5 Ghz (Python)
172
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.
173
AIMC-RUC
91.45 %
96.94 %
86.28 %
0.11 s
1 core @ 2.5 Ghz (Python)
174
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.
175
FPC3D_all
91.42 %
95.52 %
86.76 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
176
CU-PointRCNN
91.34 %
97.25 %
86.98 %
0.1 s
GPU @ 1.5 Ghz (Python + C/C++)
177
deprecated
91.31 %
96.90 %
83.91 %
0.06 s
GPU @ >3.5 Ghz (Python)
178
SC(DLA34+DCO)
91.27 %
96.61 %
83.50 %
0.07 s
GPU @ 2.5 Ghz (Python)
179
GAA
91.20 %
94.50 %
82.97 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
180
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.
181
IOU-SSD
code
91.18 %
94.25 %
87.58 %
0.045s
1 core @ 2.5 Ghz (C/C++)
182
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.
183
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.
184
anonymous
91.08 %
96.57 %
82.86 %
1 s
1 core @ 2.5 Ghz (C/C++)
185
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.
186
FII-CenterNet
91.03 %
94.48 %
83.00 %
0.09 s
GPU @ 2.5 Ghz (Python)
187
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.
188
MonoFlex
91.02 %
96.01 %
83.38 %
0.03 s
GPU @ 2.5 Ghz (Python)
189
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.
190
MonoEF
code
90.88 %
96.32 %
83.27 %
0.03 s
1 core @ 2.5 Ghz (Python)
191
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.
192
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.
193
DLE
90.81 %
93.83 %
80.93 %
0.04 s
GPU @ 2.5 Ghz (Python)
194
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.
195
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.
196
OCM3D
90.70 %
94.36 %
84.56 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
197
Simple3D Net
90.70 %
93.54 %
87.81 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
198
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.
199
yolo4
90.63 %
94.71 %
80.38 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
200
NF2
90.62 %
94.14 %
81.30 %
0.1 s
GPU @ 2.5 Ghz (Python)
201
baseline
90.59 %
93.29 %
87.18 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
202
Det3D
90.54 %
94.35 %
84.40 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
203
FADNet
code
90.49 %
96.15 %
80.71 %
0.04 s
GPU @ >3.5 Ghz (Python)
204
IGRP+
90.42 %
96.03 %
87.63 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
205
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.
206
yolo4_5l
code
90.38 %
91.79 %
80.64 %
0.02 s
1 core @ 2.5 Ghz (Python + C/C++)
207
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.
208
APL-Second
90.20 %
93.20 %
82.95 %
0.05 s
1 core @ 2.5 Ghz (Python)
209
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.
210
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.
211
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.
212
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.
213
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.
214
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.
215
LCA
89.94 %
93.40 %
82.76 %
0.05 s
1 core @ 2.5 Ghz (Python)
216
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.
217
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.
218
MonoGeo
89.77 %
94.68 %
80.00 %
0.05 s
1 core @ 2.5 Ghz (Python)
219
MCA
89.72 %
93.42 %
79.96 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
220
UDI-mono3D
89.67 %
94.39 %
80.29 %
0.05 s
1 core @ 2.5 Ghz (Python)
221
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.
222
IAFA
89.46 %
93.08 %
79.83 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
223
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.
224
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.
225
R-FCN(FPN)
89.35 %
93.53 %
79.35 %
0.2 s
1 core @ 2.5 Ghz (Python)
226
Scan_YOLO
88.95 %
90.69 %
79.85 %
0.1 s
4 cores @ 3.0 Ghz (Python)
227
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.
228
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.
229
EACV
88.70 %
94.51 %
81.15 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
230
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.
231
PMN
88.65 %
93.64 %
77.94 %
0.2 s
1 core @ 2.5 Ghz (Python)
232
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.
233
BFF
88.49 %
90.84 %
78.84 %
8.4 s
4 cores @ 1.5 Ghz (Python)
234
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.
235
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.
236
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.
237
tiny-stereo-v2
88.38 %
96.52 %
81.01 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
238
AACL
88.35 %
93.56 %
73.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
239
stereo-tkc
88.30 %
96.49 %
80.94 %
0.4 s
GPU @ 2.0 Ghz (Python + C/C++)
240
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.
241
UDI-mono3D
88.16 %
93.93 %
79.57 %
0.05 s
1 core @ 2.5 Ghz (Python)
242
anonymous
88.16 %
96.22 %
75.72 %
1 s
1 core @ 2.5 Ghz (C/C++)
243
tiny-stereo
88.16 %
96.49 %
80.74 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
244
CDI3D
87.97 %
91.46 %
80.14 %
0.03 s
GPU @ 2.5 Ghz (Python)
245
MonoRUn
87.91 %
95.48 %
78.10 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
246
Multi-task DG
87.72 %
95.50 %
75.51 %
0.06 s
GPU @ 2.5 Ghz (Python)
247
Object Transformer
87.67 %
93.33 %
79.98 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
248
MMCOM
87.58 %
95.08 %
77.48 %
0.04 s
1 core @ 2.5 Ghz (Python)
249
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.
250
DAMNET
code
87.39 %
92.48 %
82.41 %
1 s
1 core @ 2.5 Ghz (C/C++)
251
MA
87.29 %
93.21 %
79.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
252
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.
253
IMA
87.17 %
92.67 %
77.46 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
254
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.
255
yolo_rgb
86.90 %
90.01 %
77.52 %
0.07 s
GPU @ 2.5 Ghz (Python)
256
NL_M3D
86.80 %
91.31 %
72.37 %
0.2 s
1 core @ 2.5 Ghz (Python)
257
voxelrcnn
86.69 %
94.60 %
79.91 %
15 s
1 core @ 2.5 Ghz (C/C++)
258
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.
259
OSE
86.21 %
95.64 %
76.83 %
0.1 s
GPU @ 2.5 Ghz (C/C++)
260
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.
261
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.
262
PLDet3d
85.51 %
88.65 %
77.30 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
263
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.
264
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.
265
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 .
266
Center3D
85.05 %
95.14 %
73.06 %
0.05 s
GPU @ 3.5 Ghz (Python)
267
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.
268
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.
269
LGDet3d
84.95 %
87.35 %
77.05 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
270
bifpn_fsrn
84.93 %
93.68 %
74.45 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
271
ResNet-RRC (pruned)
84.93 %
89.59 %
73.26 %
0.11 s
GPU @ 1.5 Ghz (Python + C/C++)
272
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.
273
MP-Mono
84.83 %
91.58 %
65.89 %
0.16 s
GPU @ 2.5 Ghz (Python)
274
ResNet-RRC
84.81 %
89.43 %
73.18 %
0.11 s
GPU @ 1.5 Ghz (Python + C/C++)
275
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.
276
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.
277
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.
278
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.
279
OSE+
83.92 %
95.20 %
76.69 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
280
LAPNet
83.85 %
90.81 %
65.37 %
0.03 s
1 core @ 2.5 Ghz (Python)
281
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.
282
Deprecated
83.39 %
89.00 %
64.29 %
Deprecated
Deprecated
283
DAMono3D
83.36 %
88.94 %
64.23 %
0.09s
1 core @ 2.5 Ghz (C/C++)
284
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.
285
Mag
83.15 %
94.24 %
70.63 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
286
MTMono3d
83.11 %
90.55 %
75.48 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
287
SSL-RTM3D Res18
82.97 %
93.35 %
73.11 %
0.02 s
GPU @ 2.5 Ghz (Python)
288
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.
289
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.
290
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.
291
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.
292
DP3D
82.63 %
87.90 %
66.62 %
0.07 s
GPU @ 1.5 Ghz (Python + C/C++)
293
Stereo3D
82.15 %
94.81 %
62.17 %
0.1 s
GPU 1080Ti
294
LNET
82.02 %
91.49 %
67.71 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
295
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.
296
DDMP-3D
81.70 %
91.15 %
63.12 %
0.18 s
1 core @ 2.5 Ghz (Python)
297
LCD3D
81.25 %
91.29 %
64.55 %
0.03 s
GPU @ 2.5 Ghz (Python)
298
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.
299
SOD
81.18 %
94.24 %
66.44 %
0.1 s
1 core @ 2.5 Ghz (Python)
300
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.
301
CaDDN
80.73 %
93.61 %
71.09 %
0.63 s
GPU @ 2.5 Ghz (Python)
302
UM3D_TUM
80.36 %
92.88 %
65.95 %
0.05 s
1 core @ 2.5 Ghz (Python)
303
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.
304
KMC
code
79.99 %
89.71 %
73.33 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
305
YoloMono3D
code
79.63 %
92.37 %
59.69 %
0.05 s
GPU @ 2.5 Ghz (Python)
306
DA-3Ddet
79.47 %
89.49 %
63.04 %
0.4 s
GPU @ 2.5 Ghz (Python)
307
ITS-MDPL
79.20 %
92.45 %
71.88 %
0.16 s
GPU @ 2.5 Ghz (Python)
308
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.
309
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.
310
AEC3D
78.59 %
88.58 %
74.62 %
0.01 s
GPU @ 2.5 Ghz (Python)
311
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.
312
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.
313
VN3D
77.90 %
86.89 %
72.05 %
0.02 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
314
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.
315
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.
316
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.
317
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.
318
RelationNet3D
76.62 %
81.36 %
68.48 %
0.04 s
GPU @ 2.5 Ghz (Python)
319
RelationNet3D_res18
76.45 %
85.48 %
65.52 %
0.04 s
GPU @ 2.5 Ghz (Python)
320
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.
321
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.
322
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.
323
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.
324
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.
325
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.
326
yolo_depth
74.40 %
88.71 %
65.58 %
0.07 s
GPU @ 2.5 Ghz (Python)
327
RTS3D
73.08 %
80.48 %
64.02 %
0.03 s
GPU @ 2.5 Ghz (Python)
328
NCL
code
71.91 %
64.71 %
71.78 %
NA s
1 core @ 2.5 Ghz (Python)
329
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 .
330
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.
331
DAM
70.78 %
90.08 %
61.38 %
1 s
GPU @ 2.5 Ghz (Python)
332
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.
333
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.
334
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.
335
RetinaMono
code
69.01 %
75.18 %
58.98 %
0.02 s
1 core @ 2.5 Ghz (Python)
336
BirdNet+
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 . arXiv:2003.04188 [cs.CV] 2020.
337
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.
338
SparVox3D
67.88 %
83.76 %
52.56 %
0.05 s
GPU @ 2.0 Ghz (Python)
339
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.
340
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.
341
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.
342
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.
343
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.
344
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.
345
Y4
code
63.60 %
81.79 %
56.20 %
0.03 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
346
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.
347
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.
348
PG-MonoNet
62.75 %
70.87 %
54.34 %
0.19 s
GPU @ 2.5 Ghz (Python)
349
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.
350
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.
351
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.
352
FPIOD
code
60.04 %
78.81 %
50.13 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
353
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.
354
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.
355
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.
356
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.
357
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.
358
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) . .
359
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.
360
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.
361
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 .
362
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.
363
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.
364
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.
365
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.
366
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.
367
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.
368
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.
369
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.
370
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.
371
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.
372
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.
373
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.
374
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.
375
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.
376
NVNet(BEV-3D)
0.00 %
0.00 %
0.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
377
Neighbor-VoteNet
0.00 %
0.00 %
0.00 %
0.1 s
1 core @ 2.5 Ghz (Python)