Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
TED
85.28 %
91.61 %
80.68 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
2
LIVOX_Det
84.94 %
91.72 %
80.10 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
3
SFD
84.76 %
91.73 %
77.92 %
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.
4
Anonymous
84.76 %
91.99 %
79.81 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
5
CasA++
84.04 %
90.68 %
79.69 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
6
Anonymous
83.96 %
90.83 %
77.47 %
n/a s
1 core @ 2.5 Ghz (Python + C/C++)
7
DGDNH
83.88 %
90.69 %
79.50 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
8
Anonymous
83.51 %
89.08 %
78.94 %
n/a s
1 core @ 2.5 Ghz (C/C++)
9
GraR-VoI
83.27 %
91.89 %
77.78 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
10
GLENet-VR
83.23 %
91.67 %
78.43 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
11
VPFNet
83.21 %
91.02 %
78.20 %
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.
12
GraR-Po
83.18 %
91.79 %
77.98 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
13
CasA
83.06 %
91.58 %
80.08 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
14
BtcDet
code
82.86 %
90.64 %
78.09 %
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.
15
Anonymous
82.79 %
91.30 %
78.07 %
n/a s
1 core @ 2.5 Ghz (C/C++)
16
GraR-Vo
82.77 %
91.29 %
77.20 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
17
3SNet
82.70 %
89.41 %
78.03 %
0.07 s
GPU @ 2.5 Ghz (Python)
18
PE-RCVN
82.69 %
91.51 %
77.75 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
19
CAD
82.68 %
88.96 %
77.91 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
20
SPG_mini
code
82.66 %
90.64 %
77.91 %
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.
21
HCPVF
82.63 %
89.34 %
77.72 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
22
DSASNet
82.63 %
89.48 %
77.94 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
23
SA3DNet
82.57 %
90.49 %
77.88 %
0.05 s
GPU @ 2.5 Ghz (Python)
24
SE-SSD
code
82.54 %
91.49 %
77.15 %
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.
25
TF3D
82.46 %
89.10 %
77.78 %
0.1 s
2 cores @ 3.0 Ghz (Python)
26
DVF-V
82.45 %
89.40 %
77.56 %
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.
27
GraR-Pi
82.42 %
90.94 %
77.00 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
28
DVF-PV
82.40 %
90.99 %
77.37 %
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.
29
Anonymous
82.30 %
90.88 %
76.89 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
30
PVT-SSD
82.29 %
90.65 %
76.85 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
31
Focals Conv
code
82.28 %
90.55 %
77.59 %
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.
32
CLOCs
code
82.28 %
89.16 %
77.23 %
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.
33
TBD
82.23 %
88.76 %
77.48 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
34
CityBrainLab
82.22 %
90.54 %
77.19 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
35
SASA
code
82.16 %
88.76 %
77.16 %
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.
36
ImpDet
82.14 %
88.39 %
76.98 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
37
SPG
code
82.13 %
90.50 %
78.90 %
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.
38
VoTr-TSD
code
82.09 %
89.90 %
79.14 %
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.
39
TBD
82.09 %
89.50 %
79.29 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
40
Pyramid R-CNN
82.08 %
88.39 %
77.49 %
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.
41
FS-Net
82.07 %
88.68 %
77.42 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
42
VoxSeT
code
82.06 %
88.53 %
77.46 %
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.
43
SRIF-RCNN
82.04 %
88.45 %
77.54 %
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.
44
LGNet
82.02 %
90.65 %
77.34 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
45
EQ-PVRCNN
code
82.01 %
90.13 %
77.53 %
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.
46
anonymous
81.99 %
88.82 %
77.26 %
0.09 s
GPU @ 2.5 Ghz (Python)
47
Anonymous
81.96 %
89.90 %
77.20 %
0.1s
1 core @ 2.5 Ghz (C/C++)
48
EPNet++
81.96 %
91.37 %
76.71 %
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.
49
HMFI
code
81.93 %
88.90 %
77.30 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
50
PV-RCNN++
code
81.88 %
90.14 %
77.15 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
51
GLENet
81.86 %
89.87 %
77.32 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
52
PDV
code
81.86 %
90.43 %
77.36 %
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.
53
SGNet
81.85 %
88.83 %
77.47 %
0.09 s
GPU @ 2.5 Ghz (Python)
54
Anonymous
81.85 %
89.96 %
76.51 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
55
ST-RCNN
81.84 %
90.50 %
77.22 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
56
ST-RCNN (SNLW-RCNN)
code
81.84 %
90.50 %
77.22 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
57
ISE-RCNN
81.83 %
89.12 %
77.29 %
0.09 s
1 core @ 2.5 Ghz (Python + C/C++)
58
FV2P v2
81.81 %
88.17 %
77.43 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
59
Anonymous
81.80 %
89.86 %
77.26 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
60
SqueezeRCNN
81.80 %
88.72 %
77.10 %
0.08 s
1 core @ 2.5 Ghz (Python)
61
CityBrainLab-CT3D
code
81.77 %
87.83 %
77.16 %
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.
62
USVLab BSAODet
81.74 %
88.89 %
77.14 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
63
JPVNet
81.73 %
88.66 %
76.94 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
64
TBD
81.73 %
89.48 %
79.05 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
65
M3DeTR
code
81.73 %
90.28 %
76.96 %
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.
66
SIENet
code
81.71 %
88.22 %
77.22 %
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.
67
TBD
81.71 %
88.46 %
76.63 %
0.1 s
1 core @ 2.5 Ghz (Python)
68
DCCA
81.70 %
88.42 %
77.18 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
69
VCRCNN
81.68 %
90.52 %
77.26 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
70
TBD
81.68 %
87.93 %
76.92 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
71
Voxel R-CNN
code
81.62 %
90.90 %
77.06 %
0.04 s
GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance
Voxel-based 3D Object Detection
. AAAI 2021.
72
BADet
code
81.61 %
89.28 %
76.58 %
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.
73
SARFE
81.59 %
88.88 %
76.74 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
74
FromVoxelToPoint
code
81.58 %
88.53 %
77.37 %
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.
75
H^23D R-CNN
code
81.55 %
90.43 %
77.22 %
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.
76
Anonymous
81.55 %
87.90 %
77.03 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
77
DSA-PV-RCNN
code
81.46 %
88.25 %
76.96 %
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.
78
P2V-RCNN
81.45 %
88.34 %
77.20 %
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.
79
MMLab PV-RCNN
code
81.43 %
90.25 %
76.82 %
0.08 s
1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set
Abstraction for
3D Object Detection . CVPR 2020.
80
TPCG
81.41 %
89.16 %
76.90 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
81
DDet
81.38 %
89.63 %
78.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
82
XView
81.35 %
89.21 %
76.87 %
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.
83
ISE-RCNN-PV
81.34 %
88.05 %
76.99 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
84
RangeRCNN
81.33 %
88.47 %
77.09 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D
Object Detection with Range Image
Representation . arXiv preprint arXiv:2009.00206 2020.
85
CAT-Det
81.32 %
89.87 %
76.68 %
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.
86
IKT3D
80.97 %
87.84 %
76.43 %
0.05 s
1 core @ 2.5 Ghz (Python)
87
VPFNet
code
80.97 %
88.51 %
76.74 %
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.
88
GVNet-V2
80.96 %
87.57 %
76.32 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
89
VueronNet
code
80.96 %
90.06 %
73.72 %
0.06 s
1 core @ 2.0 Ghz (Python)
90
DKDet
80.94 %
87.66 %
76.23 %
0.03 s
GPU @ 2.5 Ghz (Python + C/C++)
91
FusionDetv2-v4
80.93 %
87.75 %
76.12 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
92
sa-voxel-centernet
code
80.77 %
87.39 %
76.45 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
93
Sem-Aug
80.77 %
89.41 %
75.90 %
0.08 s
GPU @ 2.5 Ghz (Python)
94
CM3DV
80.77 %
87.28 %
76.51 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
95
Associate-3Ddet_v2
80.77 %
91.53 %
75.23 %
0.04 s
1 core @ 2.5 Ghz (Python)
96
CSVoxel-RCNN
80.73 %
87.44 %
76.18 %
0.03 s
GPU @ 1.0 Ghz (Python)
97
FusionDetv2-v3
80.70 %
88.05 %
76.10 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
98
StructuralIF
80.69 %
87.15 %
76.26 %
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.
99
SPVB-SSD
80.68 %
86.99 %
76.23 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
100
CLOCs_PVCas
code
80.67 %
88.94 %
77.15 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection . 2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
101
GVNet
code
80.52 %
87.63 %
75.99 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
102
SVGA-Net
80.47 %
87.33 %
75.91 %
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.
103
SC-Voxel-RCNN
80.46 %
86.94 %
75.85 %
0.12 s
GPU @ 1.0 Ghz (Python)
104
TBD
80.44 %
88.83 %
73.18 %
0.1 s
1 core @ 2.5 Ghz (Python)
105
Sem-Aug v1
code
80.40 %
88.92 %
77.37 %
0.04 s
GPU @ 3.5 Ghz (Python)
106
SRDL
80.38 %
87.73 %
76.27 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
107
Fast-CLOCs
80.35 %
89.10 %
76.99 %
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.
108
FPV-SSD
80.34 %
87.72 %
75.40 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
109
SPANet
80.34 %
91.05 %
74.89 %
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.
110
IA-SSD (single)
code
80.32 %
88.87 %
75.10 %
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.
111
TBD
80.29 %
87.37 %
73.05 %
0.1 s
1 core @ 2.5 Ghz (Python)
112
CIA-SSD
code
80.28 %
89.59 %
72.87 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage
Object Detector From Point Cloud . AAAI 2021.
113
FusionDetv1
80.28 %
87.45 %
76.21 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
114
DVF
80.21 %
88.97 %
75.22 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
115
VCT
80.19 %
89.12 %
77.19 %
0.2 s
1 core @ 2.5 Ghz (Python)
116
PVTr
80.16 %
86.90 %
75.98 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
117
IA-SSD (multi)
code
80.13 %
88.34 %
75.04 %
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.
118
TBD
80.12 %
88.30 %
75.29 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
119
EBM3DOD
code
80.12 %
91.05 %
72.78 %
0.12 s
1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy-
Based Models . arXiv preprint arXiv:2012.04634 2020.
120
TBD
80.12 %
86.50 %
75.72 %
0.06 s
GPU @ 2.5 Ghz (Python)
121
ATT_SSD
80.11 %
88.94 %
74.91 %
0.01 s
1 core @ 2.5 Ghz (Python)
122
TBD
code
80.06 %
88.75 %
74.84 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
123
3D-CVF at SPA
80.05 %
89.20 %
73.11 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and
LiDAR
Features Using Cross-View Spatial Feature
Fusion for
3D Object Detection . ECCV 2020.
124
TBD
80.02 %
88.45 %
74.85 %
TBD
GPU @ 2.5 Ghz (Python + C/C++)
125
MVOD
80.01 %
88.53 %
77.24 %
0.16 s
1 core @ 2.5 Ghz (C/C++)
126
SIF
79.88 %
86.84 %
75.89 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
P. An: SIF . Submitted to CVIU 2021.
127
RangeIoUDet
79.80 %
88.60 %
76.76 %
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.
128
SA-SSD
code
79.79 %
88.75 %
74.16 %
0.04 s
1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud . CVPR 2020.
129
DGT-Det3D
79.78 %
86.76 %
75.73 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
130
KpNet
79.75 %
88.92 %
72.17 %
0.42 s
1 core @ 2.5 Ghz (C/C++)
131
KpNet
79.74 %
88.88 %
72.13 %
42 s
1 core @ 2.5 Ghz (C/C++)
132
STD
code
79.71 %
87.95 %
75.09 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for
Point Cloud . ICCV 2019.
133
MGAF-3DSSD
code
79.68 %
88.16 %
72.39 %
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.
134
TBD
79.67 %
88.33 %
74.30 %
0.03 s
GPU @ 2.5 Ghz (Python)
135
mbdf-netv1
code
79.66 %
90.19 %
74.76 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
136
PTA-RCNN
79.61 %
87.84 %
74.43 %
0.08 s
1 core @ 2.5 Ghz (Python)
137
Struc info fusion II
79.59 %
88.97 %
72.51 %
0.05 s
GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion . Submitted to CVIU 2021.
138
3DSSD
code
79.57 %
88.36 %
74.55 %
0.04 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object
Detector . CVPR 2020.
139
EBM3DOD baseline
code
79.52 %
88.80 %
72.30 %
0.05 s
1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy-
Based Models . arXiv preprint arXiv:2012.04634 2020.
140
Struc info fusion I
79.49 %
88.70 %
74.25 %
0.05 s
1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion . Submitted to CVIU 2021.
141
Point-GNN
code
79.47 %
88.33 %
72.29 %
0.6 s
GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D
Object Detection in a Point Cloud . CVPR 2020.
142
SECOND
79.46 %
87.44 %
73.97 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
143
AGS-SSD[la]
79.39 %
88.13 %
74.11 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
144
SSL-PointGNN
code
79.36 %
87.78 %
74.15 %
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.
145
NV-RCNN
79.32 %
87.58 %
74.74 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
146
USVLab BSAODet (S)
79.30 %
88.02 %
76.05 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
147
EPNet
code
79.28 %
89.81 %
74.59 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection . ECCV 2020.
148
DVFENet
79.18 %
86.20 %
74.58 %
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.
149
ITCA-SSD
code
79.11 %
88.66 %
72.12 %
0.05 s
1 core @ 2.5 Ghz (Python)
150
Faraway-Frustum
code
79.05 %
87.45 %
76.14 %
0.1 s
GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion . 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
151
3D IoU-Net
79.03 %
87.96 %
72.78 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for
Point Clouds . arXiv preprint arXiv:2004.04962 2020.
152
SERCNN
78.96 %
87.74 %
74.30 %
0.1 s
1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and
Object Detection for Autonomous Driving . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2020.
153
NV2P-RCNN
78.92 %
87.36 %
74.16 %
0.1 s
GPU @ 2.5 Ghz (Python)
154
MSADet
78.81 %
88.31 %
73.82 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
155
CSNet8306
code
78.74 %
89.57 %
72.09 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
156
MVAF-Net
code
78.71 %
87.87 %
75.48 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for
3D Object Detection . arXiv preprint arXiv:2011.00652 2020.
157
CenterFuse
78.70 %
86.92 %
73.87 %
0.059 sec/frame
2 x V100
158
MMLab-PartA^2
code
78.49 %
87.81 %
73.51 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from
Point Cloud with Part-aware and Part-aggregation
Network . IEEE Transactions on Pattern Analysis and
Machine Intelligence 2020.
159
CLOCs_SecCas
78.45 %
86.38 %
72.45 %
0.1 s
1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates
Fusion for 3D Object Detection . 2020 IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
160
CSNet
78.42 %
87.39 %
71.75 %
0.1 s
1 core @ 2.5 Ghz (Python)
161
FusionDetv2-v2
78.42 %
86.59 %
73.87 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
162
Patches - EMP
78.41 %
89.84 %
73.15 %
0.5 s
GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D
Object Detection . arXiv preprint arXiv:1910.04093 2019.
163
HotSpotNet
78.31 %
87.60 %
73.34 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots . Proceedings of the European Conference
on Computer Vision (ECCV) 2020.
164
FusionDetv2-v5
78.30 %
86.94 %
73.44 %
0.05 s
1 core @ 2.5 Ghz (Java + C/C++)
165
Sem-Aug-PointRCNN++
78.06 %
86.69 %
73.85 %
0.1 s
8 cores @ 3.0 Ghz (Python)
166
CF-cd-io-tv
78.05 %
86.38 %
73.29 %
1 s
1 core @ 2.5 Ghz (C/C++)
167
3D-VDNet
78.05 %
87.13 %
72.90 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
168
VPN
77.93 %
85.02 %
72.97 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
169
CenterNet3D
77.90 %
86.20 %
73.03 %
0.04 s
GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous
Driving . 2020.
170
TBD
77.85 %
86.46 %
72.39 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
171
CF-ctdep-tv-ta
77.75 %
85.27 %
74.83 %
1 s
1 core @ 2.5 Ghz (C/C++)
172
IoU-2B
77.74 %
85.65 %
71.30 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
173
Reprod-Two-Branch
77.73 %
85.60 %
74.24 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
174
CVFNet
77.70 %
88.75 %
71.95 %
28.1ms
1 core @ 2.5 Ghz (Python)
175
AutoAlign
77.58 %
86.84 %
73.23 %
0.1 s
1 core @ 2.5 Ghz (Python)
176
TBD
77.56 %
85.38 %
72.32 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
177
CFF-tv
77.53 %
85.01 %
74.01 %
1 s
1 core @ 2.5 Ghz (C/C++)
178
TCDVF
77.49 %
85.55 %
72.29 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
179
CFF-ep25
77.48 %
84.84 %
72.96 %
1 s
1 core @ 2.5 Ghz (C/C++)
180
UberATG-MMF
77.43 %
88.40 %
70.22 %
0.08 s
GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D
Object Detection . CVPR 2019.
181
CFF-tv-v2
77.41 %
85.18 %
72.81 %
1 s
1 core @ 2.5 Ghz (C/C++)
182
Associate-3Ddet
code
77.40 %
85.99 %
70.53 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual
Association for 3D Point Cloud Object Detection . The IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) 2020.
183
Fast Point R-CNN
77.40 %
85.29 %
70.24 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN . Proceedings of the IEEE international
conference on computer vision (ICCV) 2019.
184
cff-tv-v2-ep25
77.38 %
84.44 %
72.82 %
1 s
1 core @ 2.5 Ghz (C/C++)
185
3D_att
77.27 %
88.46 %
70.11 %
0.17 s
GPU @ 2.5 Ghz (Python)
186
cp-tv-kp-io-sc
77.25 %
85.41 %
72.42 %
1 s
1 core @ 2.5 Ghz (C/C++)
187
Patches
77.20 %
88.67 %
71.82 %
0.15 s
GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D
Object Detection . arXiv preprint arXiv:1910.04093 2019.
188
CF-ctdep-tv
77.12 %
84.71 %
74.02 %
1 s
1 core @ 2.5 Ghz (C/C++)
189
CF-base-tv
77.09 %
83.72 %
73.71 %
1 s
1 core @ 2.5 Ghz (C/C++)
190
Sem-Aug-PointRCNN
code
77.04 %
82.75 %
73.21 %
0.1 s
GPU @ 3.5 Ghz (C/C++)
191
KeyFuse2B
76.95 %
84.86 %
72.53 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
192
KeyPoint-IoUHead
76.81 %
84.61 %
72.16 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
193
HRI-VoxelFPN
76.70 %
85.64 %
69.44 %
0.02 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature
aggregation in 3D object detection from point
clouds . sensors 2020.
194
DKAnet
76.70 %
84.57 %
71.54 %
0.05 s
1 core @ 2.0 Ghz (Python)
195
cff-tv-t
76.68 %
85.58 %
70.69 %
1 s
1 core @ 2.5 Ghz (C/C++)
196
SARPNET
76.64 %
85.63 %
71.31 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal
Network for LiDAR-based 3D Object Detection . Neurocomputing 2019.
197
Anonymous
76.60 %
85.29 %
71.77 %
1
1 core @ 2.5 Ghz (Python)
198
DTFI
76.59 %
85.29 %
71.78 %
0.03 s
1 core @ 2.5 Ghz (Python)
199
CSNet8299
code
76.55 %
86.49 %
71.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
200
3D IoU Loss
76.50 %
86.16 %
71.39 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection . International Conference on 3D
Vision
(3DV) 2019.
201
HNet-3DSSD
code
76.48 %
86.06 %
69.71 %
0.05 s
GPU @ 2.5 Ghz (Python)
202
F-ConvNet
code
76.39 %
87.36 %
66.69 %
0.47 s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to
Aggregate Local Point-Wise Features for Amodal 3D
Object Detection . IROS 2019.
203
variance_point
76.27 %
87.44 %
72.03 %
0.05 s
1 core @ 2.5 Ghz (Python)
204
SegVoxelNet
76.13 %
86.04 %
70.76 %
0.04 s
1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context
and
Depth-aware Features for 3D Vehicle Detection from
Point Cloud . ICRA 2020.
205
S-AT GCN
76.04 %
83.20 %
71.17 %
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.
206
KPP3D
code
76.00 %
86.66 %
71.07 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
207
TANet
code
75.94 %
84.39 %
68.82 %
0.035s
GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from
Point Clouds with Triple Attention . AAAI 2020.
208
CF-base-train
75.93 %
83.47 %
71.22 %
1 s
1 core @ 2.5 Ghz (C/C++)
209
cp-tv-kp
75.85 %
83.50 %
72.54 %
1 s
1 core @ 2.5 Ghz (C/C++)
210
PointRGCN
75.73 %
85.97 %
70.60 %
0.26 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
211
cp-tv
75.67 %
83.31 %
72.24 %
1 s
1 core @ 2.5 Ghz (C/C++)
212
MMLab-PointRCNN
code
75.64 %
86.96 %
70.70 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation
and
detection from point cloud . Proceedings of the IEEE Conference
on
Computer Vision and Pattern Recognition 2019.
213
Self-Calib Conv
75.59 %
83.54 %
71.96 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
214
AB3DMOT
code
75.43 %
86.10 %
68.88 %
0.0047s
1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object
Tracking . arXiv:1907.03961 2019.
215
CF-ctdep-train
75.43 %
83.03 %
71.31 %
1 s
1 core @ 2.5 Ghz (C/C++)
216
R-GCN
75.26 %
83.42 %
68.73 %
0.16 s
GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for
3D Vehicles Detection Refinement . ArXiv 2019.
217
epBRM
code
75.15 %
85.00 %
69.84 %
0.1 s
GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection
by Spatial Transformation Mechanism . arXiv preprint arXiv:1910.04853 2019.
218
MAFF-Net(DAF-Pillar)
75.04 %
85.52 %
67.61 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D
Vehicle Detection with Multi-modal Adaptive Feature
Fusion . arXiv preprint arXiv:2009.10945 2020.
219
sscl-20p
74.82 %
86.06 %
69.87 %
0.02 s
1 core @ 2.5 Ghz (Python)
220
PI-RCNN
74.82 %
84.37 %
70.03 %
0.1 s
1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D
Object Detector with Point-based Attentive Cont-conv
Fusion Module . AAAI 2020 : The Thirty-Fourth
AAAI Conference on Artificial Intelligence 2020.
221
MF
74.70 %
83.42 %
66.51 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
222
LazyTorch-CP-Infer-O
74.57 %
81.82 %
70.24 %
1 s
1 core @ 2.5 Ghz (C/C++)
223
LazyTorch-CP-Small-P
74.44 %
81.73 %
70.14 %
1 s
1 core @ 2.5 Ghz (C/C++)
224
City-CF-fixed
74.37 %
83.23 %
69.65 %
1 s
1 core @ 2.5 Ghz (C/C++)
225
PointPillars
code
74.31 %
82.58 %
68.99 %
16 ms
1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from
Point Clouds . CVPR 2019.
226
ARPNET
74.04 %
84.69 %
68.64 %
0.08 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network
for 3D object detection . Science China Information Sciences 2019.
227
CenterPoint (pcdet)
73.96 %
81.17 %
69.48 %
0.051 sec/frame
2 x V100
228
PC-CNN-V2
73.79 %
85.57 %
65.65 %
0.5 s
GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles . 2018 IEEE International Conference on Robotics
and Automation (ICRA) 2018.
229
C-GCN
73.62 %
83.49 %
67.01 %
0.147 s
GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D
Vehicles Detection Refinement . ArXiv 2019.
230
3DBN
73.53 %
83.77 %
66.23 %
0.13s
1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object
Detection . CoRR 2019.
231
Dune-DCF-e11
73.51 %
80.89 %
68.89 %
1 s
1 core @ 2.5 Ghz (C/C++)
232
CrazyTensor-CP
73.50 %
81.04 %
69.87 %
1 s
1 core @ 2.5 Ghz (Python)
233
PointRGBNet
73.49 %
83.99 %
68.56 %
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.
234
City-CF
73.48 %
80.85 %
69.07 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
235
RangeDet
code
73.44 %
80.53 %
67.28 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
236
PSM_stereo
73.43 %
81.28 %
66.98 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
237
Dune-DCF-e15
73.29 %
80.34 %
68.54 %
1 s
1 core @ 2.5 Ghz (C/C++)
238
SCNet
73.17 %
83.34 %
67.93 %
0.04 s
GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud . IEEE Access 2019.
239
Dune-DCF-e09
73.15 %
80.40 %
68.57 %
1 s
1 core @ 2.5 Ghz (C/C++)
240
AFTD
73.12 %
82.71 %
68.09 %
1 s
1 core @ 2.5 Ghz (Python + C/C++)
241
PP-PCdet
code
73.07 %
83.32 %
68.18 %
0.01 s
1 core @ 2.5 Ghz (Python)
242
PFF3D
code
72.93 %
81.11 %
67.24 %
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.
243
CrazyTensor-CF
72.92 %
79.87 %
68.41 %
1 s
1 core @ 2.5 Ghz (C/C++)
244
DASS
72.31 %
81.85 %
65.99 %
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.
245
HS3D
code
72.25 %
83.57 %
67.49 %
0.12 s
1 core @ 2.5 Ghz (Python + C/C++)
246
TBD_BD
code
72.16 %
83.36 %
66.87 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
247
TBD
71.94 %
83.20 %
66.83 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
248
AVOD-FPN
code
71.76 %
83.07 %
65.73 %
0.1 s
Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation . IROS 2018.
249
PointPainting
71.70 %
82.11 %
67.08 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object
Detection . CVPR 2020.
250
Contrastive PP
code
71.64 %
84.80 %
66.49 %
0.01 s
1 core @ 2.5 Ghz (Python)
251
new_stereo
70.79 %
80.05 %
66.04 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
252
WS3D
70.59 %
80.99 %
64.23 %
0.1 s
GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection
from Lidar Point Cloud . 2020.
253
F-PointNet
code
69.79 %
82.19 %
60.59 %
0.17 s
GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data . arXiv preprint arXiv:1711.08488 2017.
254
FusionDetv2-baseline
68.87 %
79.05 %
63.68 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
255
UberATG-ContFuse
68.78 %
83.68 %
61.67 %
0.06 s
GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor
3D Object Detection . ECCV 2018.
256
MLOD
code
67.76 %
77.24 %
62.05 %
0.12 s
GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method . arXiv preprint arXiv:1909.04163 2019.
257
DSGN++
code
67.37 %
83.21 %
59.91 %
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.
258
DisposalNet
67.33 %
77.55 %
62.44 %
0.2 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
259
Anonymous
66.97 %
83.77 %
58.41 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
260
AVOD
code
66.47 %
76.39 %
60.23 %
0.08 s
Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object
Detection from View Aggregation . IROS 2018.
261
StereoDistill
66.39 %
81.66 %
57.39 %
0.4 s
1 core @ 2.5 Ghz (Python)
262
FusionDetv2-v1
65.65 %
75.21 %
60.65 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
263
MMLAB LIGA-Stereo
code
64.66 %
81.39 %
57.22 %
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.
264
BirdNet+
code
64.04 %
76.15 %
59.79 %
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.
265
CZY
63.68 %
77.56 %
57.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
266
MV3D
63.63 %
74.97 %
54.00 %
0.36 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for
Autonomous
Driving . CVPR 2017.
267
SD3DOD
62.00 %
76.09 %
55.46 %
0.04 s
GPU @ 2.5 Ghz (Python)
268
AEC3D
61.99 %
72.16 %
57.11 %
18 ms
GPU @ 2.5 Ghz (Python)
269
VN3D
61.41 %
72.37 %
56.86 %
0.02 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
270
SNVC
code
61.34 %
78.54 %
54.23 %
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.
271
RCD
60.56 %
70.54 %
55.58 %
0.1 s
GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for
Scale Invariant 3D Object Detection . Conference on Robot Learning (CoRL) 2020.
272
Anonymous
58.57 %
77.81 %
52.13 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
273
A3DODWTDA
code
56.82 %
62.84 %
48.12 %
0.08 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
274
FD
56.40 %
73.05 %
52.25 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
275
PS++
code
55.28 %
74.80 %
46.70 %
PS++ s
1 core @ 2.5 Ghz (C/C++)
276
PL++ (SDN+GDC)
code
54.88 %
68.38 %
49.16 %
0.6 s
GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D
Object Detection in Autonomous Driving . International Conference on Learning
Representations 2020.
277
MV3D (LIDAR)
54.54 %
68.35 %
49.16 %
0.24 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for
Autonomous
Driving . CVPR 2017.
278
CDN
code
54.22 %
74.52 %
46.36 %
0.6 s
GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo
Disparity Estimation . Advances in Neural
Information Processing Systems (NeurIPS) 2020.
279
CG-Stereo
53.58 %
74.39 %
46.50 %
0.57 s
GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object
Detection with
Split Depth Estimation . IROS 2020.
280
PS
code
52.88 %
74.41 %
44.38 %
PS s
1 core @ 2.5 Ghz (C/C++)
281
UPF_3D
52.83 %
78.24 %
46.12 %
0.29 s
1 core @ 2.5 Ghz (Python)
282
DSGN
code
52.18 %
73.50 %
45.14 %
0.67 s
NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D
Object Detection . CVPR 2020.
283
BirdNet+ (legacy)
code
51.85 %
70.14 %
50.03 %
0.1 s
Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
284
ppt
50.41 %
54.19 %
45.14 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
285
Complexer-YOLO
47.34 %
55.93 %
42.60 %
0.06 s
GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object
Detection and Tracking on Semantic Point
Clouds . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)
Workshops 2019.
286
ESGN
46.39 %
65.80 %
38.42 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
287
Disp R-CNN (velo)
code
45.78 %
68.21 %
37.73 %
0.387 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via
Shape Prior Guided Instance Disparity Estimation . CVPR 2020.
288
CDN-PL++
44.86 %
64.31 %
38.11 %
0.4 s
GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity
Estimation . Advances in Neural Information
Processing Systems 2020.
289
Disp R-CNN
code
43.27 %
67.02 %
36.43 %
0.387 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection
via Shape Prior Guided Instance Disparity
Estimation . CVPR 2020.
290
Pseudo-LiDAR++
code
42.43 %
61.11 %
36.99 %
0.4 s
GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D
Object Detection in Autonomous Driving . International Conference on Learning
Representations 2020.
291
ART
42.42 %
63.38 %
36.44 %
20ms s
1 core @ 2.5 Ghz (C/C++)
292
YOLOStereo3D
code
41.25 %
65.68 %
30.42 %
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.
293
BEVC
40.72 %
50.05 %
36.42 %
35ms
GPU @ 1.5 Ghz (Python)
294
RT3D-GMP
38.76 %
45.79 %
30.00 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving . 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
295
ZoomNet
code
38.64 %
55.98 %
30.97 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming
Neural Network for 3D Object Detection . Proceedings of the AAAI Conference on
Artificial Intelligence 2020.
296
OC Stereo
code
37.60 %
55.15 %
30.25 %
0.35 s
1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D
Object Detection . ICRA 2020.
297
Pseudo-Lidar
code
34.05 %
54.53 %
28.25 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation:
Bridging the Gap in 3D Object Detection for Autonomous
Driving . The IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
298
Stereo CenterNet
31.30 %
49.94 %
25.62 %
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.
299
Stereo R-CNN
code
30.23 %
47.58 %
23.72 %
0.3 s
GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection
for
Autonomous Driving . CVPR 2019.
300
BirdNet
27.26 %
40.99 %
25.32 %
0.11 s
Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework
from LiDAR Information . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
301
SparseLiDAR_fusion
26.23 %
36.85 %
21.45 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
302
GCDR
23.92 %
34.89 %
19.59 %
0.28 s
1 core @ 2.5 Ghz (Python)
303
VMDet_boost
23.79 %
33.89 %
20.33 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
304
RT3DStereo
23.28 %
29.90 %
18.96 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information . Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
305
Digging_M3D
21.24 %
29.15 %
19.18 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
306
VMDet
20.95 %
30.51 %
17.85 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
307
Anonymous
20.23 %
28.29 %
17.55 %
40 s
1 core @ 2.5 Ghz (C/C++)
308
SARM3D
19.70 %
25.20 %
17.35 %
0.03 s
GPU @ 2.5 Ghz (Python)
309
RT3D
19.14 %
23.74 %
18.86 %
0.09 s
GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in
LiDAR Point Cloud for Autonomous Driving . IEEE Robotics and Automation Letters 2018.
310
MonoInsight
19.04 %
27.71 %
16.03 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
311
CMKD*
18.69 %
28.55 %
16.77 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
312
Mix-Teaching
18.54 %
26.89 %
15.79 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
313
StereoFENet
18.41 %
29.14 %
14.20 %
0.15 s
1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection
with
Feature Enhancement Networks . IEEE Transactions on Image Processing 2019.
314
anonymity
18.00 %
28.10 %
15.60 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
315
SCSTSV-MonoFlex
17.91 %
27.38 %
15.58 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
316
MoGDE
17.88 %
27.07 %
15.66 %
0.03 s
GPU @ 2.5 Ghz (Python)
317
LPCG-Monoflex
17.80 %
25.56 %
15.38 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
318
PS-fld
code
17.74 %
23.74 %
15.14 %
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.
319
MonoDDE
17.14 %
24.93 %
15.10 %
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.
320
Anonymous
17.08 %
24.43 %
15.25 %
40 s
1 core @ 2.5 Ghz (C/C++)
321
OPA-3D
code
17.05 %
24.60 %
14.25 %
0.04 s
1 core @ 3.5 Ghz (Python)
322
Mobile Stereo R-CNN
17.04 %
26.97 %
13.26 %
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.
323
anonymity
16.99 %
27.20 %
15.08 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
324
DD3D
code
16.87 %
23.19 %
14.36 %
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) .
325
Shape-Aware
16.52 %
23.84 %
13.88 %
0.05 s
1 core @ 2.5 Ghz (Python)
326
MonoCon
code
16.46 %
22.50 %
13.95 %
0.02 s
GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps
Monocular 3D Object Detection . AAAI 2022.
327
DID-M3D
16.29 %
24.40 %
13.75 %
0.04 s
1 core @ 2.5 Ghz (Python)
328
MonoDETR
code
16.26 %
24.52 %
13.93 %
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.
329
Lite-FPN-GUPNet
16.20 %
23.58 %
13.56 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
330
gupnet_se
16.10 %
23.62 %
13.41 %
0.03s
1 core @ 2.5 Ghz (C/C++)
331
zongmuDistill
16.08 %
25.11 %
13.69 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
332
MonoDistill
16.03 %
22.97 %
13.60 %
0.04 s
1 core @ 2.5 Ghz (Python)
333
MDNet
16.01 %
24.59 %
13.49 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
334
DDS
code
15.90 %
23.81 %
13.21 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
335
mono3d
code
15.73 %
23.96 %
13.35 %
TBD
TBD
336
monopd
code
15.72 %
23.51 %
13.07 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
337
OBMO_GUPNet
15.70 %
22.71 %
13.23 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
338
GPENet
code
15.44 %
22.41 %
12.84 %
0.02 s
GPU @ 2.5 Ghz (Python)
339
MonoDTR
15.39 %
21.99 %
12.73 %
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.
340
mono3d
15.26 %
23.41 %
12.80 %
0.03 s
GPU @ 2.5 Ghz (Python)
341
HBD
15.17 %
21.71 %
13.06 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
342
EW
code
15.13 %
21.16 %
12.81 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
343
ZongmuMono3d
code
15.08 %
23.79 %
13.25 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
344
GUPNet
code
15.02 %
22.26 %
13.12 %
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.
345
HomoLoss(monoflex)
code
14.94 %
21.75 %
13.07 %
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.
346
Anonymous
14.87 %
23.93 %
12.45 %
40 s
1 core @ 2.5 Ghz (C/C++)
347
Anonymous
14.84 %
22.73 %
13.08 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
348
M3DSSD++
code
14.75 %
23.61 %
11.80 %
0.16s
1 core @ 2.5 Ghz (C/C++)
349
MonoFlex
14.73 %
22.29 %
12.77 %
0.03 s
1 core @ 2.5 Ghz (Python)
350
SGM3D
14.65 %
22.46 %
12.97 %
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.
351
Anonymous
code
14.56 %
20.65 %
11.92 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
352
SAIC_ADC_Mono3D
code
14.54 %
18.98 %
12.86 %
50 s
GPU @ 2.5 Ghz (Python)
353
CA3D
14.49 %
20.89 %
12.19 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
354
MonoEdge
14.47 %
21.08 %
12.73 %
0.05 s
GPU @ 2.5 Ghz (Python)
355
MDSNet
14.46 %
24.30 %
11.12 %
0.07 s
1 core @ 2.5 Ghz (Python)
356
MonoGround
14.36 %
21.37 %
12.62 %
0.03 s
1 core @ 2.5 Ghz (Python)
357
MonoEdge-RCNN
14.35 %
19.74 %
11.94 %
0.05 s
1 core @ 2.5 Ghz (Python)
358
ANM
14.33 %
20.84 %
11.61 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
359
DLE
code
14.33 %
24.23 %
10.30 %
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.
360
SwinMono3D
14.24 %
22.61 %
10.11 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
361
AutoShape
code
14.17 %
22.47 %
11.36 %
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.
362
MonoEdge-Rotate
14.13 %
21.60 %
12.27 %
0.05 s
GPU @ 2.5 Ghz (Python)
363
EM
code
14.00 %
22.93 %
11.26 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
364
MAOLoss
code
14.00 %
20.05 %
11.81 %
0.05 s
1 core @ 2.5 Ghz (Python)
365
E2E-DA
13.97 %
19.73 %
11.82 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
366
MonoFlex
13.89 %
19.94 %
12.07 %
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.
367
MonoEF
13.87 %
21.29 %
11.71 %
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.
368
MonoAug
13.85 %
20.06 %
11.43 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
369
DEPT
13.83 %
20.43 %
11.66 %
0.03 s
1 core @ 2.5 Ghz (Python)
370
K3D
13.80 %
20.04 %
11.67 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
371
none
13.79 %
18.84 %
11.52 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
372
DFR-Net
13.63 %
19.40 %
10.35 %
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.
373
MonoFar
13.52 %
18.08 %
11.58 %
0.04 s
1 core @ 2.5 Ghz (Python)
374
CaDDN
code
13.41 %
19.17 %
11.46 %
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.
375
PCT
code
13.37 %
21.00 %
11.31 %
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.
376
Ground-Aware
code
13.25 %
21.65 %
9.91 %
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.
377
MK3D
13.19 %
20.48 %
11.10 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
378
Aug3D-RPN
12.99 %
17.82 %
9.78 %
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.
379
HomoLoss(imvoxelnet)
code
12.99 %
20.10 %
10.50 %
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.
380
DDMP-3D
12.78 %
19.71 %
9.80 %
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.
381
RetinaMono
12.73 %
19.41 %
10.45 %
0.02 s
1 core @ 2.5 Ghz (Python)
382
Kinematic3D
code
12.72 %
19.07 %
9.17 %
0.12 s
1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in
Monocular Video . ECCV 2020 .
383
M3DGAF
12.66 %
19.48 %
10.99 %
0.07 s
1 core @ 2.5 Ghz (Python)
384
MonoRCNN
code
12.65 %
18.36 %
10.03 %
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.
385
MP-Mono
12.37 %
17.89 %
9.58 %
0.16 s
GPU @ 2.5 Ghz (Python)
386
GrooMeD-NMS
code
12.32 %
18.10 %
9.65 %
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
MonoRUn
code
12.30 %
19.65 %
10.58 %
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.
388
monodle
code
12.26 %
17.23 %
10.29 %
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 .
389
LT-M3OD
12.26 %
18.15 %
10.05 %
0.03 s
1 core @ 2.5 Ghz (Python)
390
PPTrans
12.06 %
19.79 %
10.48 %
0.2 s
GPU @ 2.5 Ghz (Python)
391
YoloMono3D
code
12.06 %
18.28 %
8.42 %
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.
392
IAFA
12.01 %
17.81 %
10.61 %
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
GAC3D
12.00 %
17.75 %
9.15 %
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.
394
CMAN
11.87 %
17.77 %
9.16 %
0.15 s
1 core @ 2.5 Ghz (Python)
395
PGD-FCOS3D
code
11.76 %
19.05 %
9.39 %
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.
396
D4LCN
code
11.72 %
16.65 %
9.51 %
0.2 s
GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for
Monocular 3D Object Detection . CVPR 2020.
397
MonoAug
11.47 %
16.40 %
9.26 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
398
KM3D
code
11.45 %
16.73 %
9.92 %
0.03 s
1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training . 2020.
399
RefinedMPL
11.14 %
18.09 %
8.94 %
0.15 s
GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR
for 3D Object Detection in Autonomous Driving . arXiv preprint arXiv:1911.09712 2019.
400
PatchNet
code
11.12 %
15.68 %
10.17 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation . Proceedings of the European Conference
on Computer Vision (ECCV) 2020.
401
ImVoxelNet
code
10.97 %
17.15 %
9.15 %
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.
402
COF3D
10.91 %
17.86 %
8.20 %
200 s
1 core @ 2.5 Ghz (Python + C/C++)
403
MM
10.74 %
15.80 %
8.64 %
1 s
1 core @ 2.5 Ghz (C/C++)
404
AM3D
10.74 %
16.50 %
9.52 %
0.4 s
GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color-
Embedded 3D Reconstruction for Autonomous Driving . Proceedings of the IEEE international
Conference on Computer Vision (ICCV) 2019.
405
Lite-FPN
10.64 %
15.32 %
8.59 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
406
Keypoint-3D
10.42 %
15.97 %
7.91 %
14 s
1 core @ 2.5 Ghz (C/C++)
407
RTM3D
code
10.34 %
14.41 %
8.77 %
0.05 s
GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection
from Object Keypoints for Autonomous Driving . 2020.
408
E2E-DA-Lite (Res18)
10.32 %
15.56 %
8.89 %
0.01 s
GPU @ 2.5 Ghz (Python)
409
MonoPair
9.99 %
13.04 %
8.65 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection
Using Pairwise Spatial Relationships . The IEEE Conference on Computer Vision
and Pattern Recognition (CVPR) 2020.
410
Neighbor-Vote
9.90 %
15.57 %
8.89 %
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.
411
SMOKE
code
9.76 %
14.03 %
7.84 %
0.03 s
GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object
Detection via Keypoint Estimation . 2020.
412
M3D-RPN
code
9.71 %
14.76 %
7.42 %
0.16 s
GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal
Network for Object Detection . ICCV 2019 .
413
QD-3DT
code
9.33 %
12.81 %
7.86 %
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
TopNet-HighRes
9.28 %
12.67 %
7.95 %
101ms
NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in
Occupancy Grid Maps Using Deep Convolutional
Networks . 2018 21st International Conference on
Intelligent Transportation Systems (ITSC) 2018.
415
MonoCInIS
7.94 %
15.82 %
6.68 %
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.
416
Geo3D
7.70 %
11.52 %
6.80 %
0.04 s
GPU @ 2.5 Ghz (Python)
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417
SS3D
7.68 %
10.78 %
6.51 %
48 ms
Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained
End-to-End Using
Intersection-over-Union Loss . CoRR 2019.
418
MonoCInIS
7.66 %
15.21 %
6.24 %
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.
419
Mono3D_PLiDAR
code
7.50 %
10.76 %
6.10 %
0.1 s
NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with
Pseudo-LiDAR Point Cloud . arXiv:1903.09847 2019.
420
MonoPSR
code
7.25 %
10.76 %
5.85 %
0.2 s
GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging
Accurate Proposals and Shape Reconstruction . CVPR 2019.
421
Decoupled-3D
7.02 %
11.08 %
5.63 %
0.08 s
GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled
Structured Polygon Estimation and Height-Guided Depth
Estimation . AAAI 2020.
422
VoxelJones
code
6.35 %
7.39 %
5.80 %
.18 s
1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures . arXiv preprint arXiv:1907.11306 2019.
423
MonoGRNet
code
5.74 %
9.61 %
4.25 %
0.04s
NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network
for 3D Object Localization . The Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI-19) 2019.
424
A3DODWTDA (image)
code
5.27 %
6.88 %
4.45 %
0.8 s
GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without
Target Domain Annotations . 2018.
425
MonoFENet
5.14 %
8.35 %
4.10 %
0.15 s
1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object
Detection
with Feature Enhancement Networks . IEEE Transactions on Image
Processing 2019.
426
TLNet (Stereo)
code
4.37 %
7.64 %
3.74 %
0.1 s
1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from
Monocular to Stereo 3D Object Detection . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
427
CSoR
4.06 %
5.61 %
3.17 %
3.5 s
4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks
für räumliche Detektion und Klassifikation von
Objekten in Fahrzeugumgebung . 2015.
428
Shift R-CNN (mono)
code
3.87 %
6.88 %
2.83 %
0.25 s
GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D
Object Detection With Closed-form Geometric
Constraints . ICIP 2019.
429
MVRA + I-FRCNN+
3.27 %
5.19 %
2.49 %
0.18 s
GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for
Orientation Estimation . The IEEE International Conference on
Computer Vision (ICCV) Workshops 2019.
430
SparVox3D
3.20 %
5.27 %
2.56 %
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.
431
TopNet-UncEst
3.02 %
3.24 %
2.26 %
0.09 s
NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing
Object Detection Uncertainty in Multi-Layer Grid
Maps . 2019.
432
GS3D
2.90 %
4.47 %
2.47 %
2 s
1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection
Framework for Autonomous Driving . IEEE Conference on Computer Vision and
Pattern Recognition (CVPR) 2019.
433
3D-GCK
2.52 %
3.27 %
2.11 %
24 ms
Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles
from Monocular RGB Images via Geometrically
Constrained Keypoints in Real-Time . 2020 IEEE Intelligent Vehicles
Symposium (IV) 2020.
434
WeakM3D
code
2.26 %
5.03 %
1.63 %
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.
435
ROI-10D
2.02 %
4.32 %
1.46 %
0.2 s
GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape . Computer Vision and Pattern Recognition (CVPR) 2019.
436
CDTrack3D
code
1.92 %
3.20 %
1.63 %
0.0106 s
NVIDIA RTX 3090 GPU, i9 10850k CPU
437
FQNet
1.51 %
2.77 %
1.01 %
0.5 s
1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for
Monocular 3D Object Detection . Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition 2019.
438
3D-SSMFCNN
code
1.41 %
1.88 %
1.11 %
0.1 s
GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous
Driving . 2017.
439
test
code
0.03 %
0.01 %
0.03 %
50 s
1 core @ 2.5 Ghz (Python)
440
MonoDET
code
0.01 %
0.03 %
0.01 %
0.04 s
1 core @ 2.5 Ghz (Python)
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.