Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
HIKVISION-ADLab-HZ
82.83 %
89.00 %
76.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
2
SE-SSD
82.54 %
91.49 %
77.15 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
3
EA-M-RCNN(BorderAtt)
82.33 %
87.77 %
77.37 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
4
HUAWEI Octopus
82.13 %
88.26 %
77.41 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
5
ADLAB
82.08 %
90.92 %
77.36 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
6
PV-RCNN-v2
81.88 %
90.14 %
77.15 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
7
RangeRCNN-LV
81.85 %
88.76 %
77.18 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
8
PVGNet
81.81 %
89.94 %
77.09 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
9
E^2-PV-RCNN
81.70 %
88.33 %
77.20 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
10
PLNL-3DSSD
81.69 %
88.98 %
74.90 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
11
DomainAdp+PVRCNN
81.66 %
88.64 %
77.08 %
0.09 s
GPU @ 2.5 Ghz (Python)
12
Fast VP-RCNN
code
81.62 %
90.97 %
76.90 %
0.05 s
1 core @ 3.5 Ghz (C/C++)
13
Voxel R-CNN
code
81.62 %
90.90 %
77.06 %
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.
14
HyBrid Feature Det
81.59 %
88.77 %
76.92 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
15
3DIoU+++
81.58 %
88.53 %
77.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
16
CityBrainLab-TSD
81.57 %
88.13 %
77.00 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
17
CVRS VIC-RCNN
81.57 %
88.60 %
77.09 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
18
H^23D R-CNN
81.55 %
90.43 %
77.22 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
19
anonymous
code
81.55 %
90.94 %
76.74 %
0.05s
1 core @ >3.5 Ghz (python)
20
LZY_RCNN
81.52 %
88.77 %
78.59 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
21
TBD
81.51 %
88.96 %
77.27 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
22
SIENet
81.50 %
87.83 %
77.08 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
23
MSG-PGNN
81.50 %
88.70 %
76.88 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
24
DSA-PV-RCNN
81.46 %
88.25 %
76.96 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
25
MMLab PV-RCNN
code
81.43 %
90.25 %
76.82 %
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
PC-RGNN
81.38 %
87.94 %
76.88 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
27
XView
81.35 %
89.21 %
76.87 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
28
RangeRCNN
81.33 %
88.47 %
77.09 %
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.
29
FSA-PVRCNN
81.31 %
88.01 %
76.75 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
30
ReFineNet
81.24 %
87.70 %
76.77 %
0.08 s
1 core @ 2.5 Ghz (Python)
31
MSL3D
81.15 %
87.27 %
76.56 %
0.03 s
GPU @ 2.5 Ghz (Python)
32
Multi-Sensor3D
81.15 %
87.27 %
76.56 %
0.03 s
GPU @ 2.5 Ghz (Python)
33
SAA-PV-RCNN
81.09 %
87.24 %
78.05 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
34
FPC-RCNN
81.08 %
88.68 %
76.46 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
35
AIMC-RUC
80.83 %
90.14 %
73.59 %
0.11 s
1 core @ 2.5 Ghz (Python)
36
SVGA-Net
80.82 %
87.40 %
76.23 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
37
GNN-RCNN
80.81 %
87.94 %
76.53 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
38
Associate-3Ddet_v2
80.77 %
91.53 %
75.23 %
0.04 s
1 core @ 2.5 Ghz (Python)
39
CIA-SSD v2
80.71 %
89.61 %
75.06 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
40
CLOCs_PVCas
code
80.67 %
88.94 %
77.15 %
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.
41
AIMC-RUC
80.63 %
89.90 %
75.32 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
42
OAP
80.63 %
89.18 %
73.04 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
43
HRI-MSP-L
80.62 %
87.61 %
76.29 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
44
CVRS VIC-Net
80.61 %
88.25 %
75.83 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
45
CVIS-DF3D_v2
80.48 %
87.20 %
76.01 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
46
XView-PartA^2
80.41 %
87.72 %
76.22 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
47
SRDL
80.38 %
87.73 %
76.27 %
0.15 s
GPU @ 2.5 Ghz (Python + C/C++)
48
SPANet
80.34 %
91.05 %
74.89 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
49
CVRS_PF
80.33 %
88.04 %
75.21 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
50
AM-SSD
80.30 %
89.58 %
75.02 %
0.04 s
1 core @ 2.5 Ghz (Python)
51
CIA-SSD
code
80.28 %
89.59 %
72.87 %
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.
52
Baseline of CA RCNN
80.28 %
87.45 %
76.21 %
0.1 s
GPU @ 2.5 Ghz (Python)
53
CVIS-DF3D
80.28 %
87.45 %
76.21 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
54
TBD
80.24 %
87.67 %
76.27 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
55
GAP-soft-filter
80.18 %
87.43 %
76.21 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
56
CBi-GNN
80.18 %
91.50 %
74.76 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
57
TBD
80.17 %
86.83 %
75.96 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
58
deprecated
80.16 %
89.48 %
72.75 %
deprecated
deprecated
59
EBM3DOD
code
80.12 %
91.05 %
72.78 %
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.
60
3D-CVF at SPA
80.05 %
89.20 %
73.11 %
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.
61
CN
79.89 %
90.55 %
76.31 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
62
SIF
79.88 %
86.84 %
75.89 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
63
VAL
79.87 %
89.35 %
70.27 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
64
CM3DV
79.87 %
89.00 %
72.59 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
65
RangeIoUDet
79.80 %
88.60 %
76.76 %
0.02 s
1 core @ 2.5 Ghz (Python)
66
SA-SSD
code
79.79 %
88.75 %
74.16 %
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.
67
Seg-RCNN
code
79.73 %
89.16 %
72.28 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
68
CJJ
79.72 %
88.98 %
74.71 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
69
STD
code
79.71 %
87.95 %
75.09 %
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.
70
PSS
79.71 %
89.13 %
74.78 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
71
AF_V1
79.68 %
88.16 %
72.39 %
0.1 s
1 core @ 2.5 Ghz (Python)
72
FCY
79.67 %
89.19 %
74.35 %
0.02 s
GPU @ 2.5 Ghz (Python)
73
CDE-Net(0.3)
79.59 %
88.97 %
72.51 %
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.
74
3DSSD
code
79.57 %
88.36 %
74.55 %
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.
75
PointRes
79.55 %
88.73 %
74.17 %
0.013 s
1 core @ 2.5 Ghz (Python + C/C++)
76
EBM3DOD baseline
code
79.52 %
88.80 %
72.30 %
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.
77
Cas-SSD
79.50 %
88.73 %
72.46 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
78
CDE-Net(0.4)
79.49 %
88.70 %
74.25 %
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.
79
Point-GNN
code
79.47 %
88.33 %
72.29 %
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.
80
PP-3D
79.47 %
88.33 %
72.29 %
0.1 s
1 core @ 2.5 Ghz (Python)
81
nonet
79.42 %
88.28 %
75.77 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
82
RoIFusion
code
79.36 %
88.09 %
72.51 %
0.22 s
1 core @ 3.0 Ghz (Python)
83
3DIoU_v2
79.30 %
88.22 %
76.96 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
84
EPNet
code
79.28 %
89.81 %
74.59 %
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.
85
PF-GAP
79.27 %
87.65 %
76.43 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
86
FPCR-CNN
79.25 %
88.45 %
75.69 %
0.05 s
1 core @ 2.5 Ghz (Python)
87
3DIoU++
79.22 %
87.49 %
76.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
88
MGACNet
79.18 %
86.20 %
74.58 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
89
D3D
79.15 %
87.07 %
73.79 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
90
NLK-ALL
code
79.13 %
87.23 %
74.30 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
91
Faraway-Frustum
code
79.05 %
87.45 %
76.14 %
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.
92
3D IoU-Net
79.03 %
87.96 %
72.78 %
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.
93
CCFNET
78.97 %
88.20 %
74.14 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
94
SERCNN
78.96 %
87.74 %
74.30 %
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.
95
deprecated
78.83 %
87.89 %
73.52 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
96
FPC3D
78.81 %
87.61 %
75.49 %
33 s
1 core @ 2.5 Ghz (C/C++)
97
FLID
78.78 %
86.73 %
71.24 %
0.04 s
GPU @ 2.5 Ghz (Python)
98
MVAF-Net
code
78.71 %
87.87 %
75.48 %
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.
99
ISF-v2
78.67 %
87.54 %
74.03 %
0.04 s
1 core @ 2.5 Ghz (Python)
100
PVF-NET
78.58 %
87.05 %
71.68 %
0.1 s
1 core @ 2.5 Ghz (Python)
101
BLPNet_V2
78.57 %
87.10 %
71.67 %
0.04 s
1 core @ 2.5 Ghz (Python)
102
MMLab-PartA^2
code
78.49 %
87.81 %
73.51 %
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.
103
F-3DNet
78.48 %
85.48 %
71.62 %
0.5 s
GPU @ 2.5 Ghz (Python)
104
CLOCs_SecCas
78.45 %
86.38 %
72.45 %
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.
105
Patches - EMP
78.41 %
89.84 %
73.15 %
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.
106
MKFFNet
78.40 %
85.25 %
73.75 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
107
deprecated
78.32 %
89.34 %
71.21 %
0.06 s
GPU @ >3.5 Ghz (Python)
108
HotSpotNet
78.31 %
87.60 %
73.34 %
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.
109
MKFFNet
78.30 %
87.25 %
73.66 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
110
MKFFNet
78.30 %
86.86 %
73.80 %
0.01s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
111
KNN-GCNN
78.26 %
86.37 %
71.14 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
112
HV
77.92 %
86.38 %
73.04 %
0.02 s
GPU @ 2.5 Ghz (Python)
113
CenterNet3D
77.90 %
86.20 %
73.03 %
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.
114
V3D
77.87 %
86.58 %
72.52 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
115
tbd
code
77.72 %
86.09 %
72.53 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
116
VOXEL_3D
77.69 %
86.45 %
72.20 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
117
VGCN
77.65 %
84.47 %
73.36 %
0.09 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
118
UberATG-MMF
77.43 %
88.40 %
70.22 %
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.
119
Associate-3Ddet
code
77.40 %
85.99 %
70.53 %
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.
120
Fast Point R-CNN
77.40 %
85.29 %
70.24 %
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.
121
Dccnet
77.22 %
86.67 %
69.97 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
122
Patches
77.20 %
88.67 %
71.82 %
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.
123
VAR
77.08 %
84.92 %
72.21 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
124
CU-PointRCNN
76.87 %
86.55 %
73.17 %
0.1 s
GPU @ 1.5 Ghz (Python + C/C++)
125
HRI-VoxelFPN
76.70 %
85.64 %
69.44 %
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.
126
SARPNET
76.64 %
85.63 %
71.31 %
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.
127
TBD
76.57 %
85.33 %
72.05 %
0.05 s
GPU @ 2.5 Ghz (Python)
128
IGRP+
76.54 %
86.90 %
71.77 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
129
3D IoU Loss
76.50 %
86.16 %
71.39 %
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.
130
F-ConvNet
code
76.39 %
87.36 %
66.69 %
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.
131
DPointNet
76.34 %
81.67 %
70.34 %
0.07s
1 core @ 2.5 Ghz (C/C++)
132
SIEV-Net
76.18 %
85.21 %
70.60 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
133
SegVoxelNet
76.13 %
86.04 %
70.76 %
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.
134
NLK-3D
76.08 %
84.47 %
70.93 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
135
TANet
code
75.94 %
84.39 %
68.82 %
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.
136
IGRP
75.90 %
86.27 %
69.31 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
137
MVX-Net++
75.86 %
85.99 %
70.70 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
138
PointRGCN
75.73 %
85.97 %
70.60 %
0.26 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
139
MMLab-PointRCNN
code
75.64 %
86.96 %
70.70 %
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.
140
AB3DMOT
code
75.43 %
86.10 %
68.88 %
0.0047s
1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object
Tracking . arXiv:1907.03961 2019.
141
MDA
75.39 %
83.72 %
71.98 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
142
R-GCN
75.26 %
83.42 %
68.73 %
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.
143
epBRM
code
75.15 %
85.00 %
69.84 %
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.
144
3DBN_2
75.06 %
84.90 %
72.10 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
145
MAFF-Net(DAF-Pillar)
75.04 %
85.52 %
67.61 %
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.
146
PI-RCNN
74.82 %
84.37 %
70.03 %
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.
147
FPGNN
74.77 %
83.82 %
67.93 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
148
FPC3D_all
74.55 %
85.50 %
69.91 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
149
Pointpillar_TV
74.55 %
83.08 %
69.13 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
150
PointPillars
code
74.31 %
82.58 %
68.99 %
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.
151
IOU-SSD
code
74.09 %
85.00 %
68.42 %
0.045s
1 core @ 2.5 Ghz (C/C++)
152
Simple3D Net
74.06 %
83.06 %
69.17 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
153
ARPNET
74.04 %
84.69 %
68.64 %
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.
154
tt
code
73.92 %
84.14 %
69.15 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
155
PC-CNN-V2
73.79 %
85.57 %
65.65 %
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.
156
C-GCN
73.62 %
83.49 %
67.01 %
0.147 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D
Vehicles Detection Refinement . ArXiv 2019.
157
baseline
73.55 %
82.92 %
67.42 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
158
LSNet
73.55 %
86.13 %
68.58 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
159
3DBN
73.53 %
83.77 %
66.23 %
0.13s
1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object
Detection . CoRR 2019.
160
SCNet
73.17 %
83.34 %
67.93 %
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.
161
TBD
73.02 %
82.74 %
67.97 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
162
PFF3D
72.93 %
81.11 %
67.24 %
0.05 s
GPU @ 3.0 Ghz (Python + C/C++)
163
APL-Second
72.87 %
82.75 %
67.91 %
0.05 s
1 core @ 2.5 Ghz (Python)
164
DASS
72.31 %
81.85 %
65.99 %
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.
165
AVOD-FPN
code
71.76 %
83.07 %
65.73 %
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.
166
PointPainting
71.70 %
82.11 %
67.08 %
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.
167
WS3D
70.59 %
80.99 %
64.23 %
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.
168
F-PointNet
code
69.79 %
82.19 %
60.59 %
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.
169
UberATG-ContFuse
68.78 %
83.68 %
61.67 %
0.06 s
GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor
3D Object Detection . ECCV 2018.
170
MLOD
code
67.76 %
77.24 %
62.05 %
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.
171
AVOD
code
66.47 %
76.39 %
60.23 %
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.
172
DAMNET
code
65.52 %
76.25 %
59.54 %
1 s
1 core @ 2.5 Ghz (C/C++)
173
voxelrcnn
64.77 %
73.60 %
60.05 %
15 s
1 core @ 2.5 Ghz (C/C++)
174
MV3D
63.63 %
74.97 %
54.00 %
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.
175
KMC
code
62.74 %
74.45 %
56.76 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
176
RCD
60.56 %
70.54 %
55.58 %
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.
177
stereo-tkc
59.21 %
78.26 %
52.47 %
0.4 s
GPU @ 2.0 Ghz (Python + C/C++)
178
tiny-stereo-v2
57.11 %
76.87 %
50.05 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
179
A3DODWTDA
code
56.82 %
62.84 %
48.12 %
0.08 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
180
tiny-stereo
56.44 %
79.17 %
48.07 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
181
PL++ (SDN+GDC)
code
54.88 %
68.38 %
49.16 %
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.
182
MV3D (LIDAR)
54.54 %
68.35 %
49.16 %
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.
183
CDN
code
54.22 %
74.52 %
46.36 %
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.
184
CG-Stereo
53.58 %
74.39 %
46.50 %
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.
185
AEC3D
52.50 %
66.83 %
48.48 %
0.01 s
GPU @ 2.5 Ghz (Python)
186
DSGN
code
52.18 %
73.50 %
45.14 %
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.
187
VN3D
52.16 %
64.68 %
48.17 %
0.02 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188
BirdNet+
code
51.85 %
70.14 %
50.03 %
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.
189
NCL
code
50.07 %
46.58 %
50.33 %
NA s
1 core @ 2.5 Ghz (Python)
190
SOD
48.69 %
70.90 %
40.12 %
0.1 s
1 core @ 2.5 Ghz (Python)
191
Complexer-YOLO
47.34 %
55.93 %
42.60 %
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.
192
Disp R-CNN (velo)
code
45.78 %
68.21 %
37.73 %
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.
193
CDN-PL++
44.86 %
64.31 %
38.11 %
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.
194
Disp R-CNN
code
43.27 %
67.02 %
36.43 %
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.
195
OSE
43.27 %
64.78 %
37.13 %
0.1 s
GPU @ 2.5 Ghz (C/C++)
196
Pseudo-LiDAR++
code
42.43 %
61.11 %
36.99 %
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.
197
OSE+
41.60 %
62.67 %
35.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
198
Stereo3D
41.25 %
65.68 %
30.42 %
0.1 s
GPU 1080Ti
199
RT3D-GMP
38.76 %
45.79 %
30.00 %
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.
200
ZoomNet
code
38.64 %
55.98 %
30.97 %
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.
201
OC Stereo
code
37.60 %
55.15 %
30.25 %
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.
202
RTS3D
37.38 %
58.51 %
31.12 %
0.03 s
GPU @ 2.5 Ghz (Python)
203
Pseudo-Lidar
code
34.05 %
54.53 %
28.25 %
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.
204
SC(DLA34+DCO)
31.30 %
49.94 %
25.62 %
0.07 s
GPU @ 2.5 Ghz (Python)
205
Stereo R-CNN
code
30.23 %
47.58 %
23.72 %
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.
206
BirdNet
27.26 %
40.99 %
25.32 %
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.
207
NVNet(BEV-3D)
24.87 %
33.30 %
21.96 %
0.1 s
1 core @ 2.5 Ghz (Python)
208
RT3DStereo
23.28 %
29.90 %
18.96 %
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.
209
RT3D
19.14 %
23.74 %
18.86 %
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.
210
StereoFENet
18.41 %
29.14 %
14.20 %
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.
211
LGDet3d
14.82 %
22.73 %
12.88 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
212
ITS-MDPL
14.23 %
24.26 %
11.95 %
0.16 s
GPU @ 2.5 Ghz (Python)
213
MonoFlex
13.89 %
19.94 %
12.07 %
0.03 s
GPU @ 2.5 Ghz (Python)
214
MonoEF
code
13.87 %
21.29 %
11.71 %
0.03 s
1 core @ 2.5 Ghz (Python)
215
CaDDN
13.41 %
19.17 %
11.46 %
0.63 s
GPU @ 2.5 Ghz (Python)
216
Det3D
13.26 %
24.00 %
9.94 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
217
PLDet3d
12.85 %
20.72 %
11.11 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
218
DDMP-3D
12.78 %
19.71 %
9.80 %
0.18 s
1 core @ 2.5 Ghz (Python)
219
Kinematic3D
code
12.72 %
19.07 %
9.17 %
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 .
220
DAMono3D
12.66 %
16.99 %
9.97 %
0.09s
1 core @ 2.5 Ghz (C/C++)
221
RelationNet3D
12.60 %
17.57 %
10.95 %
0.04 s
GPU @ 2.5 Ghz (Python)
222
Object Transformer
12.58 %
17.87 %
10.87 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
223
MonoGeo
12.57 %
16.87 %
11.16 %
0.05 s
1 core @ 2.5 Ghz (Python)
224
MTMono3d
12.44 %
18.54 %
10.09 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
225
MonoRUn
12.30 %
19.65 %
10.58 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
226
Deprecated
12.30 %
16.48 %
9.14 %
Deprecated
Deprecated
227
DLE
12.26 %
17.23 %
10.29 %
0.04 s
GPU @ 2.5 Ghz (Python)
228
DP3D
12.24 %
18.84 %
8.96 %
0.07 s
GPU @ 1.5 Ghz (Python + C/C++)
229
YoloMono3D
code
12.06 %
18.28 %
8.42 %
0.05 s
GPU @ 2.5 Ghz (Python)
230
IAFA
12.01 %
17.81 %
10.61 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
231
D4LCN
code
11.72 %
16.65 %
9.51 %
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.
232
GA-Aug
11.67 %
17.46 %
9.69 %
0.04 s
GPU @ 2.5 Ghz (Python)
233
MP-Mono
11.65 %
16.78 %
9.01 %
0.16 s
GPU @ 2.5 Ghz (Python)
234
MCA
11.63 %
18.46 %
10.24 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
235
RetinaMono
code
11.61 %
16.68 %
9.57 %
0.02 s
1 core @ 2.5 Ghz (Python)
236
PG-MonoNet
11.51 %
15.91 %
9.01 %
0.19 s
GPU @ 2.5 Ghz (Python)
237
DA-3Ddet
11.50 %
16.77 %
8.93 %
0.4 s
GPU @ 2.5 Ghz (Python)
238
TBD
11.47 %
19.53 %
9.17 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
239
NL_M3D
11.46 %
17.54 %
8.98 %
0.2 s
1 core @ 2.5 Ghz (Python)
240
KM3D
code
11.45 %
16.73 %
9.92 %
0.03 s
1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training . 2020.
241
IMA
11.34 %
16.24 %
9.44 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
242
CDI3D
11.32 %
15.70 %
9.26 %
0.03 s
GPU @ 2.5 Ghz (Python)
243
LAPNet
11.29 %
18.02 %
8.50 %
0.03 s
1 core @ 2.5 Ghz (Python)
244
LNET
11.21 %
12.79 %
9.94 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
245
RefinedMPL
11.14 %
18.09 %
8.94 %
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.
246
UM3D_TUM
11.13 %
15.30 %
9.31 %
0.05 s
1 core @ 2.5 Ghz (Python)
247
PatchNet
code
11.12 %
15.68 %
10.17 %
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.
248
AM3D
10.74 %
16.50 %
9.52 %
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.
249
OCM3D
10.44 %
17.48 %
7.87 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
250
RTM3D
code
10.34 %
14.41 %
8.77 %
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.
251
MA
10.21 %
14.90 %
8.78 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
252
MonoPair
9.99 %
13.04 %
8.65 %
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.
253
FADNet
code
9.92 %
16.37 %
8.05 %
0.04 s
GPU @ >3.5 Ghz (Python)
254
SMOKE
code
9.76 %
14.03 %
7.84 %
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.
255
M3D-RPN
code
9.71 %
14.76 %
7.42 %
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 .
256
modat3D
9.33 %
12.81 %
7.86 %
0.03 s
GPU @ 2.5 Ghz (Python)
257
RelationNet3D_res18
9.31 %
13.37 %
8.29 %
0.04 s
GPU @ 2.5 Ghz (Python)
258
Center3D
9.31 %
12.01 %
8.06 %
0.05 s
GPU @ 3.5 Ghz (Python)
259
TopNet-HighRes
9.28 %
12.67 %
7.95 %
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.
260
LCD3D
9.04 %
13.77 %
7.23 %
0.03 s
GPU @ 2.5 Ghz (Python)
261
SSL-RTM3D Res18
8.39 %
12.65 %
7.12 %
0.02 s
GPU @ 2.5 Ghz (Python)
262
SS3D
7.68 %
10.78 %
6.51 %
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.
263
anonymous
7.66 %
15.21 %
6.24 %
1 s
1 core @ 2.5 Ghz (C/C++)
264
Mono3D_PLiDAR
code
7.50 %
10.76 %
6.10 %
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.
265
MonoPSR
code
7.25 %
10.76 %
5.85 %
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.
266
Decoupled-3D
7.02 %
11.08 %
5.63 %
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.
267
anonymous
6.77 %
13.18 %
5.63 %
1 s
1 core @ 2.5 Ghz (C/C++)
268
VoxelJones
code
6.35 %
7.39 %
5.80 %
.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.
269
MonoGRNet
code
5.74 %
9.61 %
4.25 %
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.
270
A3DODWTDA (image)
code
5.27 %
6.88 %
4.45 %
0.8 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
271
MonoFENet
5.14 %
8.35 %
4.10 %
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.
272
TLNet (Stereo)
code
4.37 %
7.64 %
3.74 %
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.
273
AACL
4.18 %
5.62 %
3.34 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
274
CSoR
4.06 %
5.61 %
3.17 %
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.
275
Shift R-CNN (mono)
code
3.87 %
6.88 %
2.83 %
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.
276
MVRA + I-FRCNN+
3.27 %
5.19 %
2.49 %
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.
277
SparVox3D
3.20 %
5.27 %
2.56 %
0.05 s
GPU @ 2.0 Ghz (Python)
278
TopNet-UncEst
3.02 %
3.24 %
2.26 %
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.
279
GS3D
2.90 %
4.47 %
2.47 %
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.
280
3D-GCK
2.52 %
3.27 %
2.11 %
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.
281
ROI-10D
2.02 %
4.32 %
1.46 %
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.
282
FQNet
1.51 %
2.77 %
1.01 %
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.
283
3D-SSMFCNN
code
1.41 %
1.88 %
1.11 %
0.1 s
GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous
Driving . 2017.
284
UDI-mono3D
0.72 %
0.62 %
0.53 %
0.05 s
1 core @ 2.5 Ghz (Python)
285
UDI-mono3D
0.41 %
0.51 %
0.43 %
0.05 s
1 core @ 2.5 Ghz (Python)
286
PVNet
0.00 %
0.00 %
0.00 %
0,1 s
1 core @ 2.5 Ghz (Python)
287
mBoW
0.00 %
0.00 %
0.00 %
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.