Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
GraR-Po
92.12 %
95.79 %
87.11 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
2
TED
92.05 %
95.44 %
87.30 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
3
LIVOX_Det
92.05 %
95.60 %
89.22 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
4
VPFNet
91.86 %
93.02 %
86.94 %
0.06 s
2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection
with Virtual Point based LiDAR and Stereo Data
Fusion . 2021.
5
SFD
code
91.85 %
95.64 %
86.83 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D
Detection with Depth Completion . CVPR 2022.
6
SE-SSD
code
91.84 %
95.68 %
86.72 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object
Detector From Point Cloud . CVPR 2021.
7
GraR-Vo
91.72 %
95.27 %
86.51 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
8
PVT-SSD
91.63 %
95.23 %
86.43 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
9
CityBrainLab
91.62 %
94.78 %
86.68 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
10
SPANet
91.59 %
95.59 %
86.53 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network
for 3D Object Detection . Pacific Rim International Conference on Artificial
Intelligence 2021.
11
CasA
91.54 %
95.19 %
86.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
12
GraR-Pi
91.52 %
95.06 %
86.42 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
13
DGDNH
91.36 %
95.03 %
88.79 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
14
BADet
code
91.32 %
95.23 %
86.48 %
0.14 s
1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object
Detection
from Point Clouds . Pattern Recognition 2022.
15
Anonymous
91.32 %
95.42 %
88.38 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
16
CasA++
91.22 %
94.57 %
88.43 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
17
Anonymous
91.14 %
94.04 %
86.33 %
n/a s
1 core @ 2.5 Ghz (C/C++)
18
SGFusion
91.11 %
94.76 %
86.27 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
19
Anonymous
91.04 %
94.76 %
86.31 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
20
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.
21
anonymous
90.90 %
92.96 %
86.34 %
0.09 s
GPU @ 2.5 Ghz (Python)
22
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.
23
VueronNet
code
90.56 %
94.67 %
85.31 %
0.06 s
1 core @ 2.0 Ghz (Python)
24
ST-RCNN
90.53 %
94.58 %
86.08 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
25
VPFNet
code
90.52 %
93.94 %
86.25 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network
for Multi-class 3D Object Detection . 2021.
26
PDV
code
90.48 %
94.56 %
86.23 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection . CVPR 2022.
27
VCRCNN
90.42 %
94.55 %
86.11 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
28
M3DeTR
code
90.37 %
94.41 %
85.98 %
n/a s
GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi-
scale, Mutual-relation 3D Object Detection with
Transformers . 2021.
29
TBD
90.37 %
93.82 %
87.72 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
30
DDet
90.34 %
94.16 %
86.01 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
31
VoTr-TSD
code
90.34 %
94.03 %
86.14 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection . ICCV 2021.
32
DSA-PV-RCNN
code
90.13 %
92.42 %
85.93 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection . 2021.
33
XView
90.12 %
92.27 %
85.94 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D
Object Detector . 2021.
34
GraR-VoI
90.10 %
95.69 %
86.85 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
35
CAT-Det
90.07 %
92.59 %
85.82 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer
for Multi-modal 3D Object Detection . CVPR 2022.
36
IKT3D
90.06 %
92.14 %
85.74 %
0.05 s
1 core @ 2.5 Ghz (Python)
37
FPV-SSD
89.93 %
91.45 %
85.21 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
38
SVGA-Net
89.88 %
92.07 %
85.59 %
0.03s
1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention
Network for 3D Object Detection from Point
Clouds . AAAI 2022.
39
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.
40
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.
41
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.
42
PE-RCVN
89.79 %
95.55 %
84.78 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
43
Anonymous
89.76 %
95.41 %
86.42 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
44
GLENet-VR
89.76 %
93.48 %
84.89 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
45
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.
46
HCPVF
89.62 %
93.20 %
86.72 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
47
DSASNet
89.59 %
93.41 %
84.81 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
48
3SNet
89.58 %
93.26 %
84.80 %
0.07 s
GPU @ 2.5 Ghz (Python)
49
CAD
89.57 %
93.03 %
84.71 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
50
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.
51
ImpDet
89.55 %
92.74 %
84.41 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
52
Struc info fusion II
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: Struc info fusion . Submitted to CVIU 2021.
53
KpNet
89.53 %
93.34 %
81.95 %
0.42 s
1 core @ 2.5 Ghz (C/C++)
54
SASA
code
89.51 %
92.87 %
86.35 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction
for Point-based 3D Object Detection . arXiv preprint arXiv:2201.01976 2022.
55
Fast-CLOCs
89.49 %
93.03 %
86.40 %
0.1 s
GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR
Object Candidates Fusion for 3D Object Detection . Proceedings of the IEEE/CVF
Winter Conference on Applications of Computer
Vision (WACV) 2022.
56
KpNet
89.49 %
93.29 %
81.92 %
42 s
1 core @ 2.5 Ghz (C/C++)
57
IA-SSD (single)
code
89.48 %
93.14 %
84.42 %
0.013 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly
Efficient Point-based Detectors for 3D LiDAR Point
Clouds . CVPR 2022.
58
CLOCs
code
89.48 %
92.91 %
86.42 %
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.
59
SA3DNet
89.46 %
93.11 %
84.60 %
0.05 s
GPU @ 2.5 Ghz (Python)
60
DVF-V
89.42 %
93.12 %
86.50 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object
Detection . 2022.
61
Struc info fusion I
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: Struc info fusion . Submitted to CVIU 2021.
62
JPVNet
89.36 %
92.78 %
84.37 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
63
BtcDet
code
89.34 %
92.81 %
84.55 %
0.09 s
GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded
Shapes for 3D Object Detection . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
64
IA-SSD (multi)
code
89.33 %
92.79 %
84.35 %
0.014 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly
Efficient Point-based Detectors for 3D LiDAR Point
Clouds . CVPR 2022.
65
Anonymous
89.27 %
92.79 %
86.53 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
66
TBD
89.24 %
92.59 %
85.99 %
0.1 s
1 core @ 2.5 Ghz (Python)
67
ATT_SSD
89.22 %
92.82 %
85.90 %
0.01 s
1 core @ 2.5 Ghz (Python)
68
TBD
code
89.21 %
92.88 %
85.87 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
69
DVF-PV
89.20 %
93.08 %
86.28 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object
Detection . 2022.
70
TF3D
89.19 %
93.10 %
84.41 %
0.1 s
2 cores @ 3.0 Ghz (Python)
71
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.
72
FS-Net
89.18 %
92.88 %
84.39 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
73
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.
74
HMFI
code
89.17 %
93.04 %
86.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
75
SSL-PointGNN
code
89.16 %
92.92 %
83.99 %
0.56 s
GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow
Backbone . arXiv preprint arXiv:2205.00705 2022.
76
Anonymous
89.15 %
92.47 %
85.97 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
77
SGNet
89.14 %
93.04 %
86.54 %
0.09 s
GPU @ 2.5 Ghz (Python)
78
DGCN
89.14 %
92.62 %
83.90 %
0.1 s
GPU @ 2.5 Ghz (Python)
79
USVLab BSAODet
89.13 %
92.92 %
86.41 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
80
SPG_mini
code
89.12 %
92.80 %
86.27 %
0.09 s
GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for
3D Object Detection via Semantic Point
Generation . Proceedings of the IEEE conference on
computer vision and pattern recognition (ICCV) 2021.
81
ITCA-SSD
code
89.12 %
93.19 %
83.99 %
0.05 s
1 core @ 2.5 Ghz (Python)
82
EQ-PVRCNN
code
89.09 %
94.55 %
86.42 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud
Understanding . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 2022.
83
SPT
89.09 %
94.87 %
84.38 %
0.1 s
GPU @ 2.5 Ghz (Python)
84
MSADet
89.08 %
92.76 %
85.66 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
85
VoxSeT
code
89.07 %
92.70 %
86.29 %
33 ms
1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach
to 3D Object Detection from Point Clouds . CVPR 2022.
86
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.
87
EPNet++
89.00 %
95.41 %
85.73 %
0.1 s
GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for
Multi-Modal 3D Object Detection . arXiv preprint arXiv:2112.11088 2021.
88
Focals Conv
code
89.00 %
92.67 %
86.33 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object
Detection . Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition 2022.
89
SECOND
88.98 %
92.01 %
83.67 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
90
LGNet
88.98 %
92.83 %
86.26 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
91
ISE-RCNN
88.97 %
92.86 %
86.28 %
0.09 s
1 core @ 2.5 Ghz (Python + C/C++)
92
PTA-RCNN
88.94 %
92.32 %
85.63 %
0.08 s
1 core @ 2.5 Ghz (Python)
93
GV-RCNN
code
88.94 %
94.52 %
86.24 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
94
TBD
88.94 %
92.03 %
86.35 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
95
SPNet
code
88.92 %
92.29 %
86.16 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
96
Sem-Aug v1
code
88.92 %
92.59 %
84.29 %
0.04 s
GPU @ 3.5 Ghz (Python)
97
H^23D R-CNN
code
88.87 %
92.85 %
86.07 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated
Hollow-3D R-CNN for 3D Object Detection . IEEE Transactions on Circuits and Systems
for Video Technology 2021.
98
Pyramid R-CNN
88.84 %
92.19 %
86.21 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and
Adaptability for 3D Object Detection . ICCV 2021.
99
CityBrainLab-CT3D
code
88.83 %
92.36 %
84.07 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel-
wise Transformer . ICCV 2021.
100
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
. AAAI 2021.
101
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.
102
GLENet
88.81 %
92.22 %
84.13 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
103
AGS-SSD[la]
88.80 %
92.61 %
85.44 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
104
FV2P v2
88.80 %
92.22 %
84.24 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
105
mbdf-netv1
code
88.77 %
94.45 %
83.90 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
106
SRIF-RCNN
88.77 %
92.10 %
86.06 %
0.0947 s
1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different
Sensors for 3D Object Detection . Applied Intelligence 2022.
107
Anonymous
88.75 %
92.09 %
84.06 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
108
PV-RCNN++
code
88.74 %
92.66 %
85.97 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
109
SPG
code
88.70 %
94.33 %
85.98 %
0.09 s
1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for
3D Object Detection via Semantic Point
Generation . Proceedings of the IEEE conference on
computer vision and pattern recognition (ICCV) 2021.
110
MVMM
code
88.70 %
92.17 %
85.47 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
111
ISE-RCNN-PV
88.69 %
92.31 %
86.10 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
112
DCAN-Second
code
88.68 %
92.76 %
85.32 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
113
SIENet
code
88.65 %
92.38 %
86.03 %
0.08 s
1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for
3D Object Detection from Point Cloud . 2021.
114
P2V-RCNN
88.63 %
92.72 %
86.14 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature
Learning for 3D Object Detection from Point
Clouds . IEEE Access 2021.
115
FromVoxelToPoint
code
88.61 %
92.23 %
86.11 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D
Object Detection for Point Cloud with Voxel-to-
Point Decoder . MM '21: The 29th ACM
International Conference on Multimedia (ACM MM) 2021.
116
RangeIoUDet
88.59 %
92.28 %
85.83 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time
3D
Object Detector Optimized by Intersection Over
Union . CVPR 2021.
117
WGVRF
88.56 %
92.45 %
85.69 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
118
DCCA
88.55 %
92.29 %
85.85 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
119
GVNet-V2
88.54 %
92.26 %
85.71 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
120
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.
121
GT3D
88.46 %
92.22 %
83.81 %
0.1 s
1 core @ 2.5 Ghz (Python)
122
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.
123
GVNet
code
88.43 %
92.19 %
85.63 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
124
USVLab BSAODet (S)
88.42 %
92.19 %
85.55 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
125
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.
126
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.
127
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.
128
StructuralIF
88.38 %
91.78 %
85.67 %
0.02 s
8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D
object detection on LiDAR-camera system . Accepted in CVIU 2021.
129
CSVoxel-RCNN
88.37 %
92.07 %
85.51 %
0.03 s
GPU @ 1.0 Ghz (Python)
130
NV-RCNN
88.36 %
91.41 %
85.72 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
131
DKDet
88.32 %
92.21 %
85.46 %
0.03 s
GPU @ 2.5 Ghz (Python + C/C++)
132
CenterFuse
88.31 %
91.54 %
83.39 %
0.059 sec/frame
2 x V100
133
SARFE
88.28 %
92.35 %
85.50 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
134
FusionDetv2-v4
88.27 %
92.05 %
85.38 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
135
TBD
88.26 %
91.44 %
85.44 %
0.06 s
GPU @ 2.5 Ghz (Python)
136
KPP3D
code
88.25 %
93.93 %
83.26 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
137
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.
138
SPVB-SSD
88.23 %
91.82 %
85.46 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
139
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.
140
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.
141
SRDL
88.17 %
92.01 %
85.43 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
142
CF-cd-io-tv
88.16 %
91.32 %
83.26 %
1 s
1 core @ 2.5 Ghz (C/C++)
143
FusionDetv1
88.13 %
91.91 %
85.40 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
144
PSA-Det3D
88.13 %
92.08 %
85.35 %
0.1 s
GPU @ 2.5 Ghz (Python)
145
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.
146
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.
147
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.
148
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.
149
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 . 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
150
NV2P-RCNN
88.08 %
93.44 %
85.32 %
0.1 s
GPU @ 2.5 Ghz (Python)
151
VPN
88.06 %
90.94 %
83.24 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
152
TBD
88.04 %
91.31 %
84.79 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
153
SC-Voxel-RCNN
88.02 %
91.45 %
85.22 %
0.12 s
GPU @ 1.0 Ghz (Python)
154
CZY
88.00 %
91.85 %
85.22 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
155
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.
156
TCDVF
87.94 %
91.21 %
84.66 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
157
DGT-Det3D
87.88 %
91.70 %
85.14 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
158
FusionDetv2-v5
87.86 %
91.92 %
83.07 %
0.05 s
1 core @ 2.5 Ghz (Java + C/C++)
159
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.
160
CSNet
87.84 %
92.23 %
82.93 %
0.1 s
1 core @ 2.5 Ghz (Python)
161
CF-ctdep-tv-ta
87.81 %
90.73 %
84.97 %
1 s
1 core @ 2.5 Ghz (C/C++)
162
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.
163
cp-tv-kp-io-sc
87.78 %
90.98 %
84.04 %
1 s
1 core @ 2.5 Ghz (C/C++)
164
SIF
87.76 %
91.44 %
85.15 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
P. An: SIF . Submitted to CVIU 2021.
165
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.
166
Reprod-Two-Branch
87.69 %
90.69 %
84.72 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
167
DKAnet
87.68 %
91.07 %
84.03 %
0.05 s
1 core @ 2.0 Ghz (Python)
168
DVFENet
87.68 %
90.93 %
84.60 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature
Extraction Network for 3D Object Detection . Neurocomputing 2021.
169
S-AT GCN
87.68 %
90.85 %
84.20 %
0.02 s
GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention
Graph Convolution Network based Feature
Enhancement for 3D Object
Detection . CoRR 2021.
170
TBD
87.67 %
91.02 %
82.42 %
0.1 s
1 core @ 2.5 Ghz (Python)
171
CFF-tv-v2
87.67 %
90.70 %
84.58 %
1 s
1 core @ 2.5 Ghz (C/C++)
172
CFF-ep25
87.66 %
90.60 %
84.71 %
1 s
1 core @ 2.5 Ghz (C/C++)
173
TBD
87.62 %
90.86 %
82.29 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
174
CF-base-tv
87.60 %
90.28 %
84.52 %
1 s
1 core @ 2.5 Ghz (C/C++)
175
AutoAlign
87.60 %
91.72 %
84.44 %
0.1 s
1 core @ 2.5 Ghz (Python)
176
KeyFuse2B
87.59 %
90.70 %
84.58 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
177
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.
178
CFF-tv
87.55 %
90.56 %
84.59 %
1 s
1 core @ 2.5 Ghz (C/C++)
179
cff-tv-v2-ep25
87.55 %
90.26 %
84.53 %
1 s
1 core @ 2.5 Ghz (C/C++)
180
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.
181
TBD
87.51 %
90.76 %
80.15 %
0.1 s
1 core @ 2.5 Ghz (Python)
182
DTFI
87.51 %
91.01 %
84.25 %
0.03 s
1 core @ 2.5 Ghz (Python)
183
CF-ctdep-tv
87.50 %
90.56 %
84.65 %
1 s
1 core @ 2.5 Ghz (C/C++)
184
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.
185
Anonymous
87.48 %
90.98 %
84.22 %
1
1 core @ 2.5 Ghz (Python)
186
MGAF-3DSSD
code
87.47 %
92.70 %
82.19 %
0.1 s
1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage
Detector with Mask-Guided Attention for Point
Cloud . MM '21: The 29th ACM
International Conference on Multimedia (ACM MM) 2021.
187
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.
188
HS3D
code
87.40 %
91.97 %
82.85 %
0.12 s
1 core @ 2.5 Ghz (Python + C/C++)
189
PVTr
87.39 %
91.21 %
84.77 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
190
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.
191
Sem-Aug
87.37 %
93.35 %
82.43 %
0.08 s
GPU @ 2.5 Ghz (Python)
192
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.
193
KeyPoint-IoUHead
87.32 %
90.36 %
83.23 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
194
ZMMPP
87.25 %
90.47 %
82.42 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
195
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.
196
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.
197
3D_att
87.09 %
93.14 %
81.92 %
0.17 s
GPU @ 2.5 Ghz (Python)
198
Contrastive PP
code
87.06 %
92.99 %
81.96 %
0.01 s
1 core @ 2.5 Ghz (Python)
199
DVF
87.05 %
92.76 %
84.13 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
200
T_PVRCNN
86.97 %
91.63 %
82.20 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
201
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.
202
cff-tv-t
86.92 %
91.04 %
80.46 %
1 s
1 core @ 2.5 Ghz (C/C++)
203
CZY_3917
86.90 %
90.69 %
82.22 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
204
CF-base-train
86.88 %
90.03 %
83.16 %
1 s
1 core @ 2.5 Ghz (C/C++)
205
Self-Calib Conv
86.86 %
90.00 %
83.88 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
206
T_PVRCNN_V2
86.85 %
91.54 %
81.82 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
207
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.
208
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.
209
IoU-2B
86.74 %
90.92 %
80.40 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
210
cp-tv-kp
86.58 %
89.58 %
83.64 %
1 s
1 core @ 2.5 Ghz (C/C++)
211
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.
212
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.
213
cp-tv
86.52 %
89.55 %
83.45 %
1 s
1 core @ 2.5 Ghz (C/C++)
214
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.
215
CF-ctdep-train
86.46 %
89.57 %
82.03 %
1 s
1 core @ 2.5 Ghz (C/C++)
216
CSNet8306
code
86.44 %
92.57 %
81.36 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
217
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.
218
Dune-DCF-e09
86.36 %
89.33 %
81.77 %
1 s
1 core @ 2.5 Ghz (C/C++)
219
Dune-DCF-e11
86.32 %
89.32 %
81.78 %
1 s
1 core @ 2.5 Ghz (C/C++)
220
PP-PCdet
code
86.32 %
89.86 %
81.62 %
0.01 s
1 core @ 2.5 Ghz (Python)
221
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.
222
Dune-DCF-e15
86.21 %
88.99 %
81.62 %
1 s
1 core @ 2.5 Ghz (C/C++)
223
TBD_BD
code
86.12 %
91.00 %
81.66 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
224
CrazyTensor-CF
86.10 %
89.13 %
81.61 %
1 s
1 core @ 2.5 Ghz (C/C++)
225
City-CF-fixed
86.09 %
89.94 %
81.73 %
1 s
1 core @ 2.5 Ghz (C/C++)
226
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.
227
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.
228
SSL_PP
code
85.93 %
92.19 %
80.40 %
16ms
GPU @ 1.5 Ghz (Python)
229
CSNet8299
code
85.91 %
91.64 %
80.95 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
230
Sem-Aug-PointRCNN++
85.88 %
91.68 %
83.37 %
0.1 s
8 cores @ 3.0 Ghz (Python)
231
DASS
85.85 %
91.74 %
80.97 %
0.09 s
1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic
Segmentation by Learning 3D Object Detection . Proceedings of the IEEE/CVF
Winter Conference on Applications of Computer
Vision (WACV) 2021.
232
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.
233
City-CF
85.83 %
89.20 %
81.61 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
234
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.
235
LazyTorch-CP-Infer-O
85.74 %
89.19 %
81.35 %
1 s
1 core @ 2.5 Ghz (C/C++)
236
PointRGBNet
85.73 %
91.39 %
80.68 %
0.08 s
4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects
Based on Multi-Sensor Information Fusion . Automotive Engineering 2022.
237
AFTD
85.63 %
90.61 %
82.28 %
1 s
1 core @ 2.5 Ghz (Python + C/C++)
238
LazyTorch-CP-Small-P
85.63 %
89.10 %
81.27 %
1 s
1 core @ 2.5 Ghz (C/C++)
239
CrazyTensor-CP
85.55 %
87.94 %
82.63 %
1 s
1 core @ 2.5 Ghz (Python)
240
Sem-Aug-PointRCNN
code
85.50 %
89.75 %
83.13 %
0.1 s
GPU @ 3.5 Ghz (C/C++)
241
variance_point
85.39 %
91.90 %
81.13 %
0.05 s
1 core @ 2.5 Ghz (Python)
242
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.
243
new_stereo
85.24 %
90.74 %
82.10 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
244
PSM_stereo
85.12 %
90.26 %
80.21 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
245
PFF3D
code
85.08 %
89.61 %
80.42 %
0.05 s
GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and
Accurate 3D Object Detection for Lidar-Camera-Based
Autonomous Vehicles Using One Shared Voxel-Based
Backbone . IEEE Access 2021.
246
CenterPoint (pcdet)
85.05 %
88.47 %
81.19 %
0.051 sec/frame
2 x V100
247
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.
248
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.
249
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.
250
MF
84.72 %
88.58 %
78.17 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
251
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.
252
FusionDetv2-baseline
84.31 %
90.38 %
79.23 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
253
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.
254
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.
255
BirdNet+
code
81.85 %
87.43 %
75.36 %
0.11 s
Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection
in LiDAR through a Sparsity-Invariant Bird’s Eye
View . IEEE Access 2021.
256
TBD
81.53 %
87.90 %
74.26 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
257
FD
81.47 %
88.34 %
75.07 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
258
CZY
81.21 %
89.10 %
76.13 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
259
AEC3D
80.37 %
86.81 %
74.26 %
18 ms
GPU @ 2.5 Ghz (Python)
260
DMF
80.29 %
84.64 %
76.05 %
0.2 s
1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for
Transportation Detection . IEEE Transactions on Intelligent
Transportation Systems 2022.
261
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.
262
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.
263
DSGN++
code
78.94 %
88.55 %
69.74 %
0.2 s
GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation
for Stereo-based 3D Detectors . arXiv preprint arXiv:2204.03039 2022.
264
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.
265
StereoDistill
78.59 %
89.03 %
69.34 %
0.4 s
1 core @ 2.5 Ghz (Python)
266
Anonymous
77.40 %
90.76 %
70.00 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
267
SD3DOD
76.96 %
86.82 %
70.05 %
0.04 s
GPU @ 2.5 Ghz (Python)
268
MMLAB LIGA-Stereo
code
76.78 %
88.15 %
67.40 %
0.4 s
1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry
Aware Representations for Stereo-based 3D
Detector . Proceedings of the IEEE/CVF
International Conference on Computer Vision
(ICCV) 2021.
269
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.
270
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.
271
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.
272
SNVC
code
73.61 %
86.88 %
64.49 %
1 s
GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper . Proceedings of the AAAI Conference on
Artificial Intelligence 2022.
273
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.
274
Anonymous
71.23 %
86.67 %
64.08 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
275
ppt
70.21 %
72.17 %
65.26 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
276
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.
277
PS++
code
68.36 %
84.64 %
59.01 %
PS++ s
1 core @ 2.5 Ghz (C/C++)
278
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.
279
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.
280
PLUME
66.27 %
82.97 %
56.70 %
0.15 s
GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from
Stereo Images . IROS 2021.
281
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.
282
PS
code
65.33 %
83.75 %
56.14 %
PS s
1 core @ 2.5 Ghz (C/C++)
283
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.
284
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.
285
UPF_3D
63.58 %
85.53 %
56.56 %
0.29 s
1 core @ 2.5 Ghz (Python)
286
BirdNet+ (legacy)
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 . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
287
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.
288
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.
289
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.
290
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.
291
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.
292
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.
293
ESGN
58.12 %
78.10 %
49.28 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
294
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.
295
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.
296
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.
297
ART
54.23 %
75.05 %
48.19 %
20ms s
1 core @ 2.5 Ghz (C/C++)
298
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.
299
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.
300
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.
301
YOLOStereo3D
code
50.28 %
76.10 %
36.86 %
0.1 s
GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for
Efficient Stereo 3D Detection . 2021 International Conference on
Robotics and Automation (ICRA) 2021.
302
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.
303
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.
304
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.
305
Stereo CenterNet
42.12 %
62.97 %
35.37 %
0.04 s
GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object
detection for autonomous driving . Neurocomputing 2022.
306
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.
307
SparseLiDAR_fusion
38.99 %
52.57 %
32.86 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
308
GCDR
37.34 %
50.85 %
30.51 %
0.28 s
1 core @ 2.5 Ghz (Python)
309
VMDet_Boost
33.13 %
46.17 %
28.80 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
310
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.
311
Anonymous
30.81 %
43.11 %
26.81 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
312
Digging_M3D
28.84 %
39.74 %
26.08 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
313
Mobile Stereo R-CNN
28.78 %
44.51 %
22.30 %
1.8 s
NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R-
CNN on Nvidia Jetson TX2 . International Conference on Advanced
Engineering, Technology and Applications
(ICAETA) 2021.
314
VMDet
28.50 %
41.41 %
23.88 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
315
Anonymous
27.70 %
37.81 %
24.61 %
40 s
1 core @ 2.5 Ghz (C/C++)
316
SARM3D
26.81 %
34.17 %
23.68 %
0.03 s
GPU @ 2.5 Ghz (Python)
317
CMKD*
25.82 %
38.98 %
22.80 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
318
MoGDE
25.60 %
38.38 %
22.91 %
0.03 s
GPU @ 2.5 Ghz (Python)
319
LPCG-Monoflex
24.81 %
35.96 %
21.86 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
320
Anonymous
24.78 %
33.38 %
22.00 %
40 s
1 core @ 2.5 Ghz (C/C++)
321
DD3Dv2
code
24.67 %
35.70 %
21.73 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
322
Mix-Teaching
24.23 %
35.74 %
20.80 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
323
MonoInsight
24.23 %
34.85 %
20.87 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
324
anonymity
23.92 %
36.92 %
21.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
325
PS-fld
code
23.76 %
32.64 %
20.64 %
0.25 s
1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object
Detection in Autonomous Driving . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
326
SCSTSV-MonoFlex
23.71 %
34.59 %
20.41 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
327
anonymity
23.61 %
36.80 %
21.31 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
328
MonoDDE
23.46 %
33.58 %
20.37 %
0.04 s
1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth
Clues for Reliable Monocular 3D Object Detection . CVPR 2022.
329
DD3D
code
23.41 %
32.35 %
20.42 %
n/a s
1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D
Object detection? . IEEE/CVF International Conference on
Computer Vision (ICCV) .
330
DID-M3D
22.76 %
32.95 %
19.83 %
0.04 s
1 core @ 2.5 Ghz (Python)
331
MonoDistill
22.59 %
31.87 %
19.72 %
0.04 s
1 core @ 2.5 Ghz (Python)
332
zongmuDistill
22.56 %
33.48 %
19.88 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
333
OPA-3D
code
22.53 %
33.54 %
19.22 %
0.04 s
1 core @ 3.5 Ghz (Python)
334
Shape-Aware
22.13 %
32.55 %
18.94 %
0.05 s
1 core @ 2.5 Ghz (Python)
335
MonoCon
code
22.10 %
31.12 %
19.00 %
0.02 s
GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps
Monocular 3D Object Detection . AAAI 2022.
336
Anonymous
22.05 %
31.75 %
19.44 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
337
gupnet_se
21.98 %
32.82 %
18.70 %
0.03s
1 core @ 2.5 Ghz (C/C++)
338
ZongmuMono3d
code
21.78 %
33.18 %
18.71 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
339
MDNet
21.71 %
33.31 %
18.49 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
340
Lite-FPN-GUPNet
21.53 %
31.68 %
18.38 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
341
DDS
code
21.50 %
32.55 %
18.25 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
342
MonoDETR
code
21.45 %
32.20 %
18.68 %
0.04 s
1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for
Monocular 3D Object Detection . arXiv preprint arXiv:2203.13310 2022.
343
OBMO_GUPNet
21.41 %
30.81 %
18.37 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
344
M3DGAF
21.39 %
31.34 %
19.28 %
0.07 s
1 core @ 2.5 Ghz (Python)
345
mono3d
code
21.39 %
32.17 %
18.47 %
TBD
TBD
346
SGM3D
21.37 %
31.49 %
18.43 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection . 2021.
347
monopd
code
21.29 %
32.12 %
18.08 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
348
DEPT
21.22 %
30.85 %
18.47 %
0.03 s
1 core @ 2.5 Ghz (Python)
349
GUPNet
code
21.19 %
30.29 %
18.20 %
NA s
1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network
for Monocular 3D Object Detection . arXiv preprint arXiv:2107.13774 2021.
350
HBD
20.91 %
29.87 %
18.22 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
351
GPENet
code
20.79 %
30.31 %
18.21 %
0.02 s
GPU @ 2.5 Ghz (Python)
352
mono3d
20.75 %
31.58 %
17.66 %
0.03 s
GPU @ 2.5 Ghz (Python)
353
LT-M3OD
20.74 %
29.40 %
17.83 %
0.03 s
1 core @ 2.5 Ghz (Python)
354
HomoLoss(monoflex)
code
20.68 %
29.60 %
17.81 %
0.04 s
1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object
Detection . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
355
MonoFlex
20.67 %
30.95 %
17.72 %
0.03 s
1 core @ 2.5 Ghz (Python)
356
Anonymous
20.47 %
33.17 %
17.31 %
40 s
1 core @ 2.5 Ghz (C/C++)
357
MonoGround
20.47 %
30.07 %
17.74 %
0.03 s
1 core @ 2.5 Ghz (Python)
358
EW
code
20.38 %
28.88 %
17.59 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
359
MonoDTR
20.38 %
28.59 %
17.14 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with
Depth-Aware Transformer . CVPR 2022.
360
MonoEdge
20.35 %
28.80 %
17.57 %
0.05 s
GPU @ 2.5 Ghz (Python)
361
SAIC_ADC_Mono3D
code
20.20 %
27.09 %
18.78 %
50 s
GPU @ 2.5 Ghz (Python)
362
MonoEdge-Rotate
20.16 %
31.19 %
17.35 %
0.05 s
GPU @ 2.5 Ghz (Python)
363
MDSNet
20.14 %
32.81 %
15.77 %
0.07 s
1 core @ 2.5 Ghz (Python)
364
AutoShape
code
20.08 %
30.66 %
15.95 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection . Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
365
MonoEdge-RCNN
20.07 %
27.62 %
16.34 %
0.05 s
1 core @ 2.5 Ghz (Python)
366
M3DSSD++
code
20.03 %
32.18 %
16.47 %
0.16s
1 core @ 2.5 Ghz (C/C++)
367
MAOLoss
code
19.95 %
28.29 %
16.94 %
0.05 s
1 core @ 2.5 Ghz (Python)
368
ANM
19.82 %
29.89 %
16.77 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
369
EM
code
19.80 %
30.61 %
16.55 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
370
MonoFlex
19.75 %
28.23 %
16.89 %
0.03 s
GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D
Object Detection . CVPR 2021.
371
MonoEF
19.70 %
29.03 %
17.26 %
0.03 s
1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An
Extrinsic Parameter Free Approach . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2021.
372
K3D
19.60 %
28.31 %
17.62 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
373
HomoLoss(imvoxelnet)
code
19.25 %
29.18 %
16.21 %
0.20 s
1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object
Detection . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition (CVPR) 2022.
374
MonoAug
19.19 %
28.20 %
16.15 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
375
MK3D
19.18 %
29.11 %
15.78 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
376
DFR-Net
19.17 %
28.17 %
14.84 %
0.18 s
1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding:
The devil is in the task: Exploiting reciprocal
appearance-localization features for monocular 3d
object detection
. ICCV 2021.
377
SwinMono3D
19.15 %
29.65 %
14.09 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
378
DLE
code
19.05 %
31.09 %
14.13 %
0.06 s
NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction . Proceedings of the British Machine Vision Conference (BMVC) 2021.
379
PCT
code
19.03 %
29.65 %
15.92 %
0.045 s
1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for
Monocular 3D Object Detection . NeurIPS 2021.
380
Anonymous
code
18.96 %
26.54 %
16.01 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
381
CaDDN
code
18.91 %
27.94 %
17.19 %
0.63 s
GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution
Network for Monocular 3D Object Detection . CVPR 2021.
382
monodle
code
18.89 %
24.79 %
16.00 %
0.04 s
GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for
Monocular 3D Object Detection . CVPR 2021 .
383
MonoFar
18.68 %
25.89 %
16.30 %
0.04 s
1 core @ 2.5 Ghz (Python)
384
none
18.66 %
26.19 %
15.79 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
385
Neighbor-Vote
18.65 %
27.39 %
16.54 %
0.1 s
GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D
Object Detection through Neighbor Distance Voting . ACM MM 2021.
386
GrooMeD-NMS
code
18.27 %
26.19 %
14.05 %
0.12 s
1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection . CVPR 2021.
387
MonoRCNN
code
18.11 %
25.48 %
14.10 %
0.07 s
GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition
for Monocular 3D Object Detection . ICCV 2021.
388
Ground-Aware
code
17.98 %
29.81 %
13.08 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object
Detection for Autonomous Driving . IEEE Robotics and Automation Letters 2021.
389
MP-Mono
17.96 %
25.36 %
13.84 %
0.16 s
GPU @ 2.5 Ghz (Python)
390
Aug3D-RPN
17.89 %
26.00 %
14.18 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth . 2021.
391
DDMP-3D
17.89 %
28.08 %
13.44 %
0.18 s
1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for
Monocular 3D Object Detection . CVPR 2020.
392
IAFA
17.88 %
25.88 %
15.35 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation
for 3D Object Detection from a Single Image . Proceedings of the Asian Conference on
Computer Vision 2020.
393
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.
394
Lite-FPN
17.58 %
26.67 %
14.61 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
395
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 .
396
MonoRUn
code
17.34 %
27.94 %
15.24 %
0.07 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation . IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
397
RetinaMono
17.33 %
26.12 %
15.26 %
0.02 s
1 core @ 2.5 Ghz (Python)
398
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.
399
YoloMono3D
code
17.15 %
26.79 %
12.56 %
0.05 s
GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for
Efficient Stereo 3D Detection . 2021 International Conference on
Robotics and Automation (ICRA) 2021.
400
CMAN
17.04 %
25.89 %
12.88 %
0.15 s
1 core @ 2.5 Ghz (Python)
401
GAC3D
16.93 %
25.80 %
12.50 %
0.25 s
1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D
object detection with
ground-guide model and adaptive convolution . 2021.
402
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.
403
MonoAug
16.71 %
24.39 %
13.83 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
404
PGD-FCOS3D
code
16.51 %
26.89 %
13.49 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth:
Detecting Objects in Perspective . Conference on Robot Learning
(CoRL) 2021.
405
ImVoxelNet
code
16.37 %
25.19 %
13.58 %
0.2 s
GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection . arXiv preprint arXiv:2106.01178 2021.
406
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.
407
MM
16.09 %
24.65 %
13.99 %
1 s
1 core @ 2.5 Ghz (C/C++)
408
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.
409
Keypoint-3D
15.54 %
23.16 %
11.83 %
14 s
1 core @ 2.5 Ghz (C/C++)
410
COF3D
15.39 %
25.36 %
11.34 %
200 s
1 core @ 2.5 Ghz (Python + C/C++)
411
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.
412
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.
413
QD-3DT
code
14.71 %
20.16 %
12.76 %
0.03 s
GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking . ArXiv:2103.07351 2021.
414
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.
415
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.
416
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.
417
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 .
418
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.
419
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.
420
MonoCInIS
11.64 %
22.28 %
9.95 %
0,13 s
GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular
3D Object Detection using Instance Segmentation . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2021.
421
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.
422
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.
423
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.
424
MonoCInIS
10.96 %
20.42 %
9.23 %
0,14 s
GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular
3D Object Detection using Instance Segmentation . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2021.
425
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.
426
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.
427
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.
428
SparVox3D
6.39 %
10.20 %
5.06 %
0.05 s
GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance
on Monocular 3D Object Detection Using Bin-Mixing
and Sparse Voxel Data . 2021 6th International
Conference on Computer Science and Engineering
(UBMK) 2021.
429
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.
430
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.
431
WeakM3D
code
5.66 %
11.82 %
4.08 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised
Monocular 3D Object Detection . ICLR 2022.
432
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.
433
CDTrack3D
code
4.61 %
7.02 %
3.73 %
0.0106 s
NVIDIA RTX 3090 GPU, i9 10850k CPU
434
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.
435
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.
436
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.
437
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 .
438
MonoDET
code
0.14 %
0.25 %
0.10 %
0.04 s
1 core @ 2.5 Ghz (Python)
439
test
code
0.09 %
0.04 %
0.11 %
50 s
1 core @ 2.5 Ghz (Python)
440
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.
441
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.