Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
SE-SSD
91.84 %
95.68 %
86.72 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
2
ADLAB
91.66 %
95.56 %
86.92 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
3
SPANet
91.59 %
95.59 %
86.53 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
4
PVGNet
91.26 %
94.36 %
86.63 %
0.05 s
1 core @ >3.5 Ghz (C/C++)
5
SA-SSD
code
91.03 %
95.03 %
85.96 %
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.
6
MMLab PV-RCNN
code
90.65 %
94.98 %
86.14 %
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.
7
CN
90.50 %
94.51 %
85.86 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
8
HyBrid Feature Det
90.35 %
92.87 %
85.87 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
9
Fast VP-RCNN
code
90.32 %
95.09 %
85.84 %
0.05 s
1 core @ 3.5 Ghz (C/C++)
10
LZY_RCNN
90.29 %
92.88 %
85.84 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
11
E^2-PV-RCNN
90.27 %
92.51 %
86.01 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
12
DomainAdp+PVRCNN
90.23 %
92.73 %
86.09 %
0.09 s
GPU @ 2.5 Ghz (Python)
13
anonymous
code
90.22 %
94.86 %
85.73 %
0.05s
1 core @ >3.5 Ghz (python)
14
MSG-PGNN
90.20 %
92.89 %
85.80 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
15
DSA-PV-RCNN
90.13 %
92.42 %
85.93 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
16
XView
90.12 %
92.27 %
85.94 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
17
FPC-RCNN
90.03 %
92.74 %
85.67 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
18
Associate-3Ddet_v2
90.00 %
95.55 %
84.72 %
0.04 s
1 core @ 2.5 Ghz (Python)
19
SAA-PV-RCNN
89.88 %
91.54 %
86.93 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
20
FSA-PVRCNN
89.87 %
92.30 %
85.71 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
21
EBM3DOD
code
89.86 %
95.64 %
84.56 %
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.
22
CIA-SSD
code
89.84 %
93.74 %
82.39 %
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.
23
HIKVISION-ADLab-HZ
89.83 %
93.21 %
84.88 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
24
AIMC-RUC
89.80 %
93.64 %
84.64 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
25
CLOCs_PVCas
code
89.80 %
93.05 %
86.57 %
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.
26
CIA-SSD v2
89.80 %
93.49 %
84.39 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
27
deprecated
89.77 %
93.68 %
82.31 %
deprecated
deprecated
28
CM3DV
89.77 %
95.54 %
84.49 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
29
EA-M-RCNN(BorderAtt)
89.76 %
94.67 %
86.73 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
30
AM-SSD
89.74 %
95.56 %
84.65 %
0.04 s
1 core @ 2.5 Ghz (Python)
31
CBi-GNN
89.74 %
95.92 %
84.54 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
32
OAP
89.72 %
93.13 %
82.25 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
33
D3D
89.72 %
93.37 %
84.72 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
34
EBM3DOD baseline
code
89.63 %
95.44 %
84.34 %
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.
35
3D-CVF at SPA
89.56 %
93.52 %
82.45 %
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.
36
CDE-Net(0.3)
89.54 %
95.26 %
82.31 %
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.
37
Cas-SSD
89.47 %
93.31 %
84.35 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
38
FCY
89.46 %
95.27 %
84.34 %
0.02 s
GPU @ 2.5 Ghz (Python)
39
PointRes
89.42 %
93.17 %
84.25 %
0.013 s
1 core @ 2.5 Ghz (Python + C/C++)
40
Seg-RCNN
code
89.39 %
93.36 %
81.93 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
41
HUAWEI Octopus
89.39 %
92.58 %
86.55 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
42
CDE-Net(0.4)
89.38 %
94.91 %
84.29 %
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.
43
PLNL-3DSSD
89.36 %
93.00 %
84.18 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
44
PSS
89.28 %
93.17 %
84.38 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
45
GNN-RCNN
89.28 %
92.13 %
85.49 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
46
CJJ
89.20 %
92.90 %
84.30 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
47
STD
code
89.19 %
94.74 %
86.42 %
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.
48
Point-GNN
code
89.17 %
93.11 %
83.90 %
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.
49
PP-3D
89.17 %
93.11 %
83.90 %
0.1 s
1 core @ 2.5 Ghz (Python)
50
RoIFusion
code
89.03 %
92.88 %
83.94 %
0.22 s
1 core @ 3.0 Ghz (Python)
51
3DSSD
code
89.02 %
92.66 %
85.86 %
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.
52
CityBrainLab-TSD
88.91 %
92.78 %
84.12 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
53
NLK-ALL
code
88.89 %
92.25 %
84.13 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
54
H^23D R-CNN
88.87 %
92.85 %
86.07 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
55
TBD
88.83 %
92.96 %
86.27 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
56
Voxel R-CNN
code
88.83 %
94.85 %
86.13 %
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.
57
HVNet
88.82 %
92.83 %
83.38 %
0.03 s
GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based
3D Object Detection . CVPR 2020.
58
RangeRCNN-LV
88.81 %
92.41 %
85.96 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
59
F-3DNet
88.76 %
92.68 %
83.63 %
0.5 s
GPU @ 2.5 Ghz (Python)
60
PV-RCNN-v2
88.74 %
92.66 %
85.97 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
61
3DIoU+++
88.61 %
92.23 %
86.11 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
62
RangeIoUDet
88.59 %
92.28 %
85.83 %
0.02 s
1 core @ 2.5 Ghz (Python)
63
SIEV-Net
88.58 %
92.27 %
83.36 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
64
ISF-v2
88.57 %
92.15 %
83.91 %
0.04 s
1 core @ 2.5 Ghz (Python)
65
KNN-GCNN
88.57 %
91.73 %
83.32 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
66
PVF-NET
88.57 %
92.20 %
83.45 %
0.1 s
1 core @ 2.5 Ghz (Python)
67
CVRS VIC-Net
88.57 %
91.94 %
85.43 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
68
BLPNet_V2
88.55 %
92.24 %
83.44 %
0.04 s
1 core @ 2.5 Ghz (Python)
69
nonet
88.49 %
91.97 %
85.33 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
70
EPNet
code
88.47 %
94.22 %
83.69 %
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.
71
CenterNet3D
88.46 %
91.80 %
83.62 %
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.
72
deprecated
88.44 %
92.14 %
85.11 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
73
SIENet
88.44 %
92.00 %
85.90 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
74
PC-RGNN
88.43 %
92.08 %
85.81 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
75
RangeRCNN
88.40 %
92.15 %
85.74 %
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.
76
Patches
88.39 %
92.72 %
83.19 %
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.
77
3D IoU-Net
88.38 %
94.76 %
81.93 %
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.
78
PF-GAP
88.35 %
92.16 %
85.64 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
79
ReFineNet
88.32 %
91.93 %
85.68 %
0.08 s
1 core @ 2.5 Ghz (Python)
80
CLOCs_SecCas
88.23 %
91.16 %
82.63 %
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.
81
MSL3D
88.23 %
91.64 %
85.53 %
0.03 s
GPU @ 2.5 Ghz (Python)
82
Multi-Sensor3D
88.23 %
91.64 %
85.53 %
0.03 s
GPU @ 2.5 Ghz (Python)
83
3DIoU_v2
88.22 %
92.52 %
85.90 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
84
NLK-3D
88.22 %
91.54 %
83.33 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
85
CVRS_PF
88.22 %
91.81 %
84.91 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
86
SVGA-Net
88.21 %
91.98 %
85.46 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
87
UberATG-MMF
88.21 %
93.67 %
81.99 %
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.
88
CVRS VIC-RCNN
88.20 %
92.35 %
85.64 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
89
Patches - EMP
88.17 %
94.49 %
84.75 %
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.
90
SRDL
88.17 %
92.01 %
85.43 %
0.15 s
GPU @ 2.5 Ghz (Python + C/C++)
91
3DIoU++
88.16 %
91.79 %
85.71 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
92
FPC3D
88.15 %
91.92 %
85.32 %
33 s
1 core @ 2.5 Ghz (C/C++)
93
Baseline of CA RCNN
88.13 %
91.91 %
85.40 %
0.1 s
GPU @ 2.5 Ghz (Python)
94
CVIS-DF3D
88.13 %
91.91 %
85.40 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
95
FPCR-CNN
88.12 %
92.62 %
85.18 %
0.05 s
1 core @ 2.5 Ghz (Python)
96
GAP-soft-filter
88.11 %
91.88 %
85.42 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
97
PointPainting
88.11 %
92.45 %
83.36 %
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.
98
SERCNN
88.10 %
94.11 %
83.43 %
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.
99
Associate-3Ddet
code
88.09 %
91.40 %
82.96 %
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.
100
HotSpotNet
88.09 %
94.06 %
83.24 %
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.
101
Faraway-Frustum
code
88.08 %
91.90 %
85.35 %
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.
102
CVIS-DF3D_v2
88.06 %
91.85 %
85.37 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
103
Dccnet
88.01 %
92.09 %
82.45 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
104
UberATG-HDNET
87.98 %
93.13 %
81.23 %
0.05 s
GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for
3D Object Detection . 2nd Conference on Robot Learning (CoRL) 2018.
105
CCFNET
87.97 %
94.25 %
83.27 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
106
HV
87.94 %
91.76 %
83.03 %
0.02 s
GPU @ 2.5 Ghz (Python)
107
AIMC-RUC
87.91 %
93.92 %
82.70 %
0.11 s
1 core @ 2.5 Ghz (Python)
108
TBD
87.89 %
91.39 %
85.24 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
109
tbd
code
87.88 %
91.36 %
84.75 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
110
XView-PartA^2
87.84 %
91.94 %
85.22 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
111
Fast Point R-CNN
87.84 %
90.87 %
80.52 %
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.
112
TBD
87.83 %
91.80 %
85.19 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
113
MMLab-PartA^2
code
87.79 %
91.70 %
84.61 %
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.
114
MKFFNet
87.78 %
91.85 %
84.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
115
FPGNN
87.78 %
92.21 %
80.86 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
116
HRI-MSP-L
87.78 %
91.74 %
85.14 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
117
SIF
87.76 %
91.44 %
85.15 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
118
MVAF-Net
code
87.73 %
91.95 %
85.00 %
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.
119
MGACNet
87.68 %
90.93 %
84.60 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
120
deprecated
87.63 %
93.66 %
80.35 %
0.06 s
GPU @ >3.5 Ghz (Python)
121
VAL
87.63 %
93.57 %
79.89 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
122
MODet
87.56 %
90.80 %
82.69 %
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.
123
VOXEL_3D
87.55 %
90.83 %
82.24 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
124
AB3DMOT
code
87.53 %
91.99 %
81.03 %
0.0047s
1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object
Tracking . arXiv:1907.03961 2019.
125
V3D
87.53 %
90.83 %
82.30 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
126
PointRGCN
87.49 %
91.63 %
80.73 %
0.26 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
127
AF_V1
87.47 %
92.70 %
82.19 %
0.1 s
1 core @ 2.5 Ghz (Python)
128
MKFFNet
87.41 %
91.62 %
84.67 %
0.01s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
129
MKFFNet
87.41 %
91.93 %
84.65 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
130
PC-CNN-V2
87.40 %
91.19 %
79.35 %
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.
131
MMLab-PointRCNN
code
87.39 %
92.13 %
82.72 %
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.
132
MAFF-Net(DAF-Pillar)
87.34 %
90.79 %
77.66 %
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.
133
VAR
87.31 %
90.68 %
82.67 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
134
DPointNet
87.29 %
88.96 %
82.61 %
0.07s
1 core @ 2.5 Ghz (C/C++)
135
HRI-VoxelFPN
87.21 %
92.75 %
79.82 %
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.
136
VGCN
87.16 %
90.67 %
82.98 %
0.09 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
137
MDA
87.13 %
90.67 %
82.80 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
138
epBRM
code
87.13 %
90.70 %
81.92 %
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.
139
Pointpillar_TV
87.08 %
90.50 %
81.98 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
140
SARPNET
86.92 %
92.21 %
81.68 %
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.
141
ARPNET
86.81 %
90.06 %
79.41 %
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.
142
C-GCN
86.78 %
91.11 %
80.09 %
0.147 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D
Vehicles Detection Refinement . ArXiv 2019.
143
FLID
86.77 %
91.58 %
81.14 %
0.04 s
GPU @ 2.5 Ghz (Python)
144
CU-PointRCNN
86.69 %
92.65 %
82.66 %
0.1 s
GPU @ 1.5 Ghz (Python + C/C++)
145
tt
code
86.68 %
90.57 %
81.98 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
146
PointPillars
code
86.56 %
90.07 %
82.81 %
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.
147
TANet
code
86.54 %
91.58 %
81.19 %
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.
148
MVX-Net++
86.53 %
91.86 %
81.41 %
0.15 s
1 core @ 2.5 Ghz (C/C++)
149
SCNet
86.48 %
90.07 %
81.30 %
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.
150
Simple3D Net
86.46 %
89.82 %
82.60 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
151
SegVoxelNet
86.37 %
91.62 %
83.04 %
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.
152
IGRP+
86.29 %
92.20 %
81.48 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
153
3D IoU Loss
86.22 %
91.36 %
81.20 %
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.
154
IGRP
86.21 %
92.04 %
81.30 %
0.18 s
1 core @ 2.5 Ghz (Python + C/C++)
155
R-GCN
86.05 %
91.91 %
81.05 %
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.
156
UberATG-PIXOR++
86.01 %
93.28 %
80.11 %
0.035 s
GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for
3D Object Detection . 2nd Conference on Robot Learning (CoRL) 2018.
157
TBD
86.00 %
89.79 %
83.37 %
0.05 s
GPU @ 2.5 Ghz (Python)
158
IOU-SSD
code
85.98 %
90.18 %
80.74 %
0.045s
1 core @ 2.5 Ghz (C/C++)
159
TBD
85.91 %
90.88 %
80.95 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
160
LSNet
85.89 %
92.12 %
80.80 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
161
DASS
85.85 %
91.74 %
80.97 %
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.
162
F-ConvNet
code
85.84 %
91.51 %
76.11 %
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.
163
PI-RCNN
85.81 %
91.44 %
81.00 %
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.
164
APL-Second
85.70 %
90.78 %
78.69 %
0.05 s
1 core @ 2.5 Ghz (Python)
165
UberATG-ContFuse
85.35 %
94.07 %
75.88 %
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.
166
3DBN_2
85.30 %
91.37 %
82.57 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
167
PFF3D
85.08 %
89.61 %
80.42 %
0.05 s
GPU @ 3.0 Ghz (Python + C/C++)
168
AVOD
code
84.95 %
89.75 %
78.32 %
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.
169
WS3D
84.93 %
90.96 %
77.96 %
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.
170
baseline
84.88 %
89.25 %
80.18 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
171
FPC3D_all
84.85 %
91.05 %
80.23 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
172
AVOD-FPN
code
84.82 %
90.99 %
79.62 %
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.
173
F-PointNet
code
84.67 %
91.17 %
74.77 %
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.
174
3DBN
83.94 %
89.66 %
76.50 %
0.13s
1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object
Detection . CoRR 2019.
175
KMC
code
83.90 %
88.87 %
76.87 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
176
MLOD
code
82.68 %
90.25 %
77.97 %
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.
177
DAMNET
code
82.14 %
87.90 %
75.52 %
1 s
1 core @ 2.5 Ghz (C/C++)
178
voxelrcnn
81.41 %
88.21 %
75.26 %
15 s
1 core @ 2.5 Ghz (C/C++)
179
UberATG-PIXOR
80.01 %
83.97 %
74.31 %
0.035 s
TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from
Point
Clouds . CVPR 2018.
180
NLK
79.15 %
82.59 %
72.65 %
0.02 s
1 core @ 2.5 Ghz (Python)
181
MV3D (LIDAR)
78.98 %
86.49 %
72.23 %
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.
182
MV3D
78.93 %
86.62 %
69.80 %
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.
183
RCD
75.83 %
82.26 %
69.61 %
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.
184
LaserNet
74.52 %
79.19 %
68.45 %
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.
185
PL++ (SDN+GDC)
code
73.80 %
84.61 %
65.59 %
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.
186
A3DODWTDA
code
73.26 %
79.58 %
62.77 %
0.08 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
187
VN3D
70.69 %
80.56 %
65.31 %
0.02 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188
AEC3D
70.66 %
81.54 %
65.27 %
0.01 s
GPU @ 2.5 Ghz (Python)
189
stereo-tkc
69.70 %
87.15 %
62.51 %
0.4 s
GPU @ 2.0 Ghz (Python + C/C++)
190
Complexer-YOLO
68.96 %
77.24 %
64.95 %
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.
191
tiny-stereo-v2
68.87 %
86.89 %
59.95 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
192
TopNet-Retina
68.16 %
80.16 %
63.43 %
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.
193
tiny-stereo
67.43 %
87.93 %
58.05 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
194
CG-Stereo
66.44 %
85.29 %
58.95 %
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.
195
PLUME
66.27 %
82.97 %
56.70 %
0.15 s
GPU @ 2.5 Ghz (Python)
196
CDN
code
66.24 %
83.32 %
57.65 %
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.
197
DSGN
code
65.05 %
82.90 %
56.60 %
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.
198
TopNet-DecayRate
64.60 %
79.74 %
58.04 %
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.
199
BirdNet+
code
63.33 %
84.80 %
61.23 %
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.
200
3D FCN
61.67 %
70.62 %
55.61 %
>5 s
1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle
Detection
in Point Cloud . IROS 2017.
201
CDN-PL++
61.04 %
81.27 %
52.84 %
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.
202
BirdNet
59.83 %
84.17 %
57.35 %
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.
203
TopNet-UncEst
59.67 %
72.05 %
51.67 %
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.
204
RT3D-GMP
59.00 %
69.14 %
45.49 %
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.
205
OSE+
58.65 %
79.80 %
50.52 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
206
Disp R-CNN (velo)
code
58.62 %
79.76 %
47.73 %
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.
207
SOD
58.50 %
81.25 %
49.08 %
0.1 s
1 core @ 2.5 Ghz (Python)
208
OSE
58.04 %
79.75 %
49.78 %
0.1 s
GPU @ 2.5 Ghz (C/C++)
209
Pseudo-LiDAR++
code
58.01 %
78.31 %
51.25 %
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.
210
Disp R-CNN
code
57.98 %
79.61 %
47.09 %
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.
211
NCL
code
57.66 %
50.87 %
57.99 %
NA s
1 core @ 2.5 Ghz (Python)
212
ZoomNet
code
54.91 %
72.94 %
44.14 %
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.
213
Neighbor-VoteNet
54.68 %
65.38 %
48.59 %
0.1 s
1 core @ 2.5 Ghz (Python)
214
VoxelJones
code
53.96 %
66.21 %
47.66 %
.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.
215
TopNet-HighRes
53.05 %
67.84 %
46.99 %
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.
216
RTS3D
51.79 %
72.17 %
43.19 %
0.03 s
GPU @ 2.5 Ghz (Python)
217
OC Stereo
code
51.47 %
68.89 %
42.97 %
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.
218
NVNet(BEV-3D)
50.41 %
61.32 %
44.74 %
0.1 s
1 core @ 2.5 Ghz (Python)
219
Stereo3D
50.28 %
76.10 %
36.86 %
0.1 s
GPU 1080Ti
220
RT3DStereo
46.82 %
58.81 %
38.38 %
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.
221
Pseudo-Lidar
code
45.00 %
67.30 %
38.40 %
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.
222
RT3D
44.00 %
56.44 %
42.34 %
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.
223
SC(DLA34+DCO)
42.12 %
62.97 %
35.37 %
0.07 s
GPU @ 2.5 Ghz (Python)
224
Stereo R-CNN
code
41.31 %
61.92 %
33.42 %
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.
225
StereoFENet
32.96 %
49.29 %
25.90 %
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.
226
LNET
29.68 %
34.30 %
25.11 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
227
Det3D
20.80 %
35.46 %
16.00 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
228
LGDet3d
20.17 %
30.72 %
16.76 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
229
ITS-MDPL
19.83 %
33.74 %
16.90 %
0.16 s
GPU @ 2.5 Ghz (Python)
230
MonoFlex
19.75 %
28.23 %
16.89 %
0.03 s
GPU @ 2.5 Ghz (Python)
231
MonoEF
code
19.70 %
29.03 %
17.26 %
0.03 s
1 core @ 2.5 Ghz (Python)
232
CaDDN
18.91 %
27.94 %
17.19 %
0.63 s
GPU @ 2.5 Ghz (Python)
233
DLE
18.89 %
24.79 %
16.00 %
0.04 s
GPU @ 2.5 Ghz (Python)
234
Object Transformer
18.78 %
26.43 %
15.94 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
235
PLDet3d
18.55 %
29.14 %
15.73 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
236
MTMono3d
18.54 %
27.00 %
15.71 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
237
MonoGeo
18.42 %
24.40 %
16.03 %
0.05 s
1 core @ 2.5 Ghz (Python)
238
DDMP-3D
17.89 %
28.08 %
13.44 %
0.18 s
1 core @ 2.5 Ghz (Python)
239
IAFA
17.88 %
25.88 %
15.35 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
240
RelationNet3D
17.66 %
25.56 %
15.52 %
0.04 s
GPU @ 2.5 Ghz (Python)
241
RefinedMPL
17.60 %
28.08 %
13.95 %
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.
242
Kinematic3D
code
17.52 %
26.69 %
13.10 %
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 .
243
MonoRUn
17.34 %
27.94 %
15.24 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
244
AM3D
17.32 %
25.03 %
14.91 %
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.
245
Deprecated
17.22 %
23.59 %
13.34 %
Deprecated
Deprecated
246
DAMono3D
17.17 %
23.73 %
13.46 %
0.09s
1 core @ 2.5 Ghz (C/C++)
247
YoloMono3D
code
17.15 %
26.79 %
12.56 %
0.05 s
GPU @ 2.5 Ghz (Python)
248
OCM3D
17.13 %
27.87 %
13.53 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
249
IMA
17.08 %
23.93 %
14.75 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
250
MCA
17.07 %
25.93 %
14.80 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
251
DP3D
16.96 %
26.51 %
12.82 %
0.07 s
GPU @ 1.5 Ghz (Python + C/C++)
252
TBD
16.93 %
29.02 %
14.58 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
253
PatchNet
code
16.86 %
22.97 %
14.97 %
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.
254
RetinaMono
code
16.85 %
24.52 %
14.02 %
0.02 s
1 core @ 2.5 Ghz (Python)
255
UM3D_TUM
16.69 %
23.63 %
14.17 %
0.05 s
1 core @ 2.5 Ghz (Python)
256
GA-Aug
16.45 %
24.64 %
14.15 %
0.04 s
GPU @ 2.5 Ghz (Python)
257
PG-MonoNet
16.31 %
23.31 %
13.03 %
0.19 s
GPU @ 2.5 Ghz (Python)
258
KM3D
code
16.20 %
23.44 %
14.47 %
0.03 s
1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training . 2020.
259
CDI3D
16.06 %
22.06 %
13.43 %
0.03 s
GPU @ 2.5 Ghz (Python)
260
D4LCN
code
16.02 %
22.51 %
12.55 %
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.
261
MP-Mono
16.01 %
23.45 %
12.07 %
0.16 s
GPU @ 2.5 Ghz (Python)
262
NL_M3D
15.93 %
24.15 %
12.11 %
0.2 s
1 core @ 2.5 Ghz (Python)
263
DA-3Ddet
15.90 %
23.35 %
12.11 %
0.4 s
GPU @ 2.5 Ghz (Python)
264
LAPNet
15.76 %
25.10 %
12.30 %
0.03 s
1 core @ 2.5 Ghz (Python)
265
MA
15.43 %
22.01 %
14.01 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
266
MonoPair
14.83 %
19.28 %
12.89 %
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.
267
Decoupled-3D
14.82 %
23.16 %
11.25 %
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.
268
modat3D
14.71 %
20.16 %
12.76 %
0.03 s
GPU @ 2.5 Ghz (Python)
269
SMOKE
code
14.49 %
20.83 %
12.75 %
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.
270
RelationNet3D_res18
14.30 %
19.93 %
12.37 %
0.04 s
GPU @ 2.5 Ghz (Python)
271
FADNet
code
14.22 %
23.00 %
12.56 %
0.04 s
GPU @ >3.5 Ghz (Python)
272
RTM3D
code
14.20 %
19.17 %
11.99 %
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.
273
LCD3D
13.99 %
21.97 %
11.43 %
0.03 s
GPU @ 2.5 Ghz (Python)
274
Center3D
13.98 %
18.89 %
12.44 %
0.05 s
GPU @ 3.5 Ghz (Python)
275
Mono3D_PLiDAR
code
13.92 %
21.27 %
11.25 %
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.
276
M3D-RPN
code
13.67 %
21.02 %
10.23 %
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 .
277
SSL-RTM3D Res18
13.37 %
19.71 %
11.10 %
0.02 s
GPU @ 2.5 Ghz (Python)
278
CSoR
13.07 %
18.67 %
10.34 %
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.
279
MonoPSR
code
12.58 %
18.33 %
9.91 %
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.
280
SS3D
11.52 %
16.33 %
9.93 %
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.
281
MonoGRNet
code
11.17 %
18.19 %
8.73 %
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.
282
MonoFENet
11.03 %
17.03 %
9.05 %
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.
283
anonymous
10.96 %
20.42 %
9.23 %
1 s
1 core @ 2.5 Ghz (C/C++)
284
anonymous
10.06 %
18.80 %
8.56 %
1 s
1 core @ 2.5 Ghz (C/C++)
285
A3DODWTDA (image)
code
8.66 %
10.37 %
7.06 %
0.8 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
286
TLNet (Stereo)
code
7.69 %
13.71 %
6.73 %
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.
287
Shift R-CNN (mono)
code
6.82 %
11.84 %
5.27 %
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.
288
AACL
6.75 %
8.55 %
5.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
289
SparVox3D
6.39 %
10.20 %
5.06 %
0.05 s
GPU @ 2.0 Ghz (Python)
290
GS3D
6.08 %
8.41 %
4.94 %
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.
291
MVRA + I-FRCNN+
5.84 %
9.05 %
4.50 %
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.
292
ROI-10D
4.91 %
9.78 %
3.74 %
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.
293
3D-GCK
4.57 %
5.79 %
3.64 %
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.
294
FQNet
3.23 %
5.40 %
2.46 %
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.
295
UDI-mono3D
3.08 %
3.93 %
2.55 %
0.05 s
1 core @ 2.5 Ghz (Python)
296
UDI-mono3D
2.79 %
3.38 %
2.37 %
0.05 s
1 core @ 2.5 Ghz (Python)
297
3D-SSMFCNN
code
2.63 %
3.20 %
2.40 %
0.1 s
GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous
Driving . 2017.
298
VeloFCN
0.14 %
0.02 %
0.21 %
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 .
299
GAA
0.00 %
0.00 %
0.00 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
300
PVNet
0.00 %
0.00 %
0.00 %
0,1 s
1 core @ 2.5 Ghz (Python)
301
multi-task CNN
0.00 %
0.00 %
0.00 %
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.
302
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.