Method
Setting
Code
Moderate
Easy
Hard
Runtime
Environment
1
VirConv-S
code
93.52 %
95.99 %
90.38 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal
3D Object Detection . CVPR 2023.
2
UDeerPEP
code
93.40 %
95.34 %
89.07 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method
for unified point cloud tasks . 2023.
3
VirConv-T
code
92.65 %
96.11 %
89.69 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal
3D Object Detection . CVPR 2023.
4
GraR-Po
code
92.12 %
95.79 %
87.11 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate
3D Object Detection with Semantic-Decorated Local
Graph . ECCV 2022.
5
TSSTDet
92.11 %
95.80 %
89.23 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object
Detection via a Spatial Shape Transformer . IEEE Sensors Journal 2024.
6
MPCF
code
92.07 %
95.92 %
87.29 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
P. Gao and P. Zhang: MPCF: Multi-Phase Consolidated Fusion for
Multi-Modal 3D Object Detection with Pseudo Point
Cloud . 2024.
7
TED
code
92.05 %
95.44 %
87.30 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object
Detection for Autonomous Driving . AAAI 2023.
8
TRTConv-L
92.04 %
95.55 %
87.23 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
9
MB3D
91.93 %
95.33 %
88.71 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
10
ViKIENet
91.87 %
95.69 %
88.99 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
11
PVFusion
code
91.87 %
95.01 %
86.96 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
12
VPFNet
code
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 . IEEE Transactions on Multimedia 2022.
13
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.
14
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.
15
LVP
91.80 %
95.49 %
88.91 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
16
ACFNet
91.78 %
92.91 %
87.06 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images . IEEE Transactions on Intelligent Vehicles 2023.
17
BVPConv-T
91.75 %
95.24 %
89.15 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
18
GraR-Vo
code
91.72 %
95.27 %
86.51 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate
3D Object Detection with Semantic-Decorated Local
Graph . ECCV 2022.
19
TRTConv-T
91.70 %
95.63 %
89.00 %
0.04 s
1 core @ 2.5 Ghz (Python + C/C++)
20
PVT-SSD
91.63 %
95.23 %
86.43 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with
Point-Voxel Transformer . CVPR 2023.
21
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.
22
ViKIENet-R
91.56 %
94.87 %
88.55 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
23
BFT3D
91.55 %
94.77 %
88.65 %
0.18 s
1 core @ 2.5 Ghz (C/C++)
24
CasA
code
91.54 %
95.19 %
86.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D
Object Detection from LiDAR point clouds . IEEE Transactions on Geoscience and
Remote Sensing 2022.
25
LoGoNet
code
91.52 %
95.48 %
87.09 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object
Detection with Local-to-Global Cross-Modal Fusion . CVPR 2023.
26
GraR-Pi
code
91.52 %
95.06 %
86.42 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate
3D Object Detection with Semantic-Decorated Local
Graph . ECCV 2022.
27
MM-UniMODE
91.51 %
95.69 %
88.71 %
0.04 s
1 core @ 2.5 Ghz (Python)
28
BVPConv-L
91.49 %
95.27 %
88.93 %
0.01 s
1 core @ 2.5 Ghz (Python + C/C++)
29
SCEMF
91.46 %
94.76 %
88.77 %
1 s
1 core @ 2.5 Ghz (C/C++)
30
V2B3D
91.40 %
94.69 %
88.46 %
0.18 s
1 core @ 2.5 Ghz (C/C++)
31
UPIDet
code
91.36 %
92.96 %
86.80 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch
for Cross-modal 3D Object Detection . Thirty-seventh Conference on Neural
Information Processing Systems 2023.
32
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.
33
ANM
code
91.30 %
94.91 %
88.51 %
ANM
ANM
34
DEF-Model
91.28 %
93.03 %
86.48 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
35
SSLFusion
91.26 %
94.86 %
88.55 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
36
CasA++
code
91.22 %
94.57 %
88.43 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D
Object Detection from LiDAR point clouds . IEEE Transactions on Geoscience and
Remote Sensing 2022.
37
OGMMDet
code
91.21 %
95.59 %
88.33 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
38
voxel_spark
code
91.18 %
94.82 %
86.58 %
0.04 s
GPU @ 2.5 Ghz (C/C++)
39
MuStD
91.13 %
94.62 %
88.28 %
67 ms
>8 cores @ 2.5 Ghz (Python)
40
spark
91.13 %
94.93 %
86.54 %
0.1 s
1 core @ 2.5 Ghz (Python)
41
3D HANet
code
91.13 %
94.33 %
86.33 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary
Network for Object Detection . IEEE Transactions on Geoscience and
Remote Sensing 2023.
42
test
91.12 %
93.93 %
86.17 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
43
DiffCandiDet
91.11 %
95.05 %
86.45 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
44
spark_voxel_rcnn
code
91.08 %
94.61 %
86.59 %
0.04 s
1 core @ 2.5 Ghz (Python)
45
voxel-rcnn+++
code
91.06 %
92.84 %
86.27 %
0.08 s
GPU @ 2.5 Ghz (Python)
46
MPC3DNet
91.03 %
95.56 %
86.36 %
0.05 s
GPU @ 1.5 Ghz (Python)
47
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.
48
L-AUG
91.00 %
94.52 %
88.08 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point
Cloud Generation for 3D Object Detection . 2023.
49
TED_S_baseline
code
90.98 %
94.56 %
86.41 %
0.09 s
1 core @ 2.5 Ghz (Python)
50
spark2
90.95 %
92.93 %
86.44 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
51
HS-fusion
90.95 %
93.77 %
87.79 %
- s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
52
Voxel_Spark_focal_we
code
90.93 %
94.83 %
86.45 %
0.08 s
1 core @ 2.5 Ghz (Python)
53
c2f
90.89 %
92.31 %
86.25 %
1 s
1 core @ 2.5 Ghz (C/C++)
54
3D Dual-Fusion
code
90.86 %
93.08 %
86.44 %
0.1 s
1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query
Camera-LiDAR Fusion for 3D Object Detection . arXiv preprint arXiv:2211.13529 2022.
55
MLFusion-VS
90.78 %
95.10 %
88.41 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
56
LCANet
90.74 %
92.59 %
86.42 %
1 s
1 core @ 2.5 Ghz (C/C++)
57
focal
90.74 %
92.58 %
88.36 %
100 s
1 core @ 2.5 Ghz (Python)
58
GEFPN
90.74 %
92.58 %
88.36 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
59
GeVo
90.74 %
92.58 %
88.36 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
60
GraphAlign(ICCV2023)
code
90.73 %
94.46 %
88.34 %
0.03 s
GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature
alignment by graph matching for multi-modal 3D
object detection . Proceedings of the IEEE/CVF
International Conference on Computer Vision 2023.
61
SDGUFusion
90.65 %
95.10 %
86.45 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
62
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.
63
SQD
code
90.63 %
95.44 %
88.04 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Z. Hou, W. Huang, Y. Hu, J. Wang and J. Yan: Sparse Query Dense: Enhancing 3D Object Detection with Pseudo Points . ACM MM Oral 2024.
64
AFFN-G
90.61 %
94.46 %
88.12 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
65
focalnet
90.61 %
94.46 %
88.12 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
66
BPG3D
90.57 %
93.00 %
86.21 %
0.05 s
1 core @ 2.5 Ghz (Python)
67
focalnet
90.56 %
94.52 %
88.08 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
68
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. C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection . IEEE Transactions on Intelligent Transportation Systems 2024.
69
ECA
90.50 %
93.87 %
85.94 %
0.08 s
GPU @ 1.5 Ghz (Python)
70
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.
71
LGNet-3classes
code
90.44 %
94.98 %
86.06 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
72
test
90.39 %
94.58 %
85.69 %
0.04 s
GPU @ 1.5 Ghz (Python + C/C++)
73
Spark_PartA2_Soft_fo
code
90.38 %
93.90 %
85.91 %
0.1 s
1 core @ 2.5 Ghz (Python)
74
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.
75
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.
76
AFFN
90.33 %
94.29 %
85.99 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
77
Spark_partA22
90.23 %
92.61 %
85.89 %
10 s
1 core @ 2.5 Ghz (Python)
78
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.
79
LumiNet
code
90.13 %
95.79 %
85.06 %
0.1 s
1 core @ 2.5 Ghz (Python)
80
LFT
90.12 %
95.83 %
85.06 %
0.1s
1 core @ 2.5 Ghz (C/C++)
81
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.
82
SFA-GCL
code
90.12 %
95.75 %
84.97 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
83
SFA-GCL(80)
code
90.11 %
95.76 %
84.96 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
84
GraR-VoI
code
90.10 %
95.69 %
86.85 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate
3D Object Detection with Semantic-Decorated Local
Graph . ECCV 2022.
85
HA-PillarNet
90.07 %
92.73 %
85.98 %
0.05 s
1 core @ 2.5 Ghz (Python)
86
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.
87
3ONet
90.07 %
95.87 %
85.09 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object
Under Obstructed Conditions . IEEE Sensors Journal 2023.
88
SFA-GCL(80, k=4)
code
90.04 %
95.67 %
84.91 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
89
spark-part2
90.01 %
93.82 %
85.89 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
90
SP_SECOND_IOU
code
89.95 %
92.23 %
85.84 %
0.04 s
1 core @ 2.5 Ghz (Python)
91
CG-SSD
89.93 %
94.26 %
85.76 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
92
Anonymous
code
89.91 %
93.38 %
84.91 %
0.04 s
1 core @ 2.5 Ghz (Python)
93
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.
94
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.
95
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.
96
MLF-DET
89.82 %
93.38 %
84.78 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross-
Modal 3D Object Detection . International Conference on
Artificial Neural Networks 2023.
97
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.
98
VoxelFSD
89.79 %
92.57 %
85.77 %
0.08 s
1 core @ 2.5 Ghz (Python)
99
GLENet-VR
code
89.76 %
93.48 %
84.89 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object
detectors
with generative label uncertainty estimation . International Journal of Computer
Vision 2023. Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness
for LiDAR-based 3D Object Detectors against
Adversarial Attacks . International Journal of Computer
Vision 2023.
100
RDIoU
code
89.75 %
94.90 %
84.67 %
0.03 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single-
stage 3D Object Detection . ECCV 2022.
101
PV-RCNN-Plus
89.75 %
91.93 %
85.77 %
1 s
1 core @ 2.5 Ghz (C/C++)
102
SFA-GCL(baseline)
code
89.74 %
95.55 %
84.63 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
103
SFA-GCL_dataaug
code
89.73 %
93.44 %
84.60 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
104
SFA-GCL
code
89.71 %
93.53 %
84.58 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
105
DGEnhCL
code
89.66 %
95.21 %
84.53 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
106
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.
107
SCNet3D
89.61 %
93.36 %
84.78 %
0.08 s
1 core @ 2.5 Ghz (Python)
108
VPA
89.61 %
95.46 %
86.81 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
109
pointpillars_spark
code
89.57 %
92.98 %
84.91 %
0.02 s
GPU @ 2.5 Ghz (C/C++)
110
3D-CVF at SPA
code
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.
111
OcTr
89.56 %
93.08 %
86.74 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object
Detection . CVPR 2023.
112
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.
113
spark_second_focal_w
89.53 %
91.19 %
85.11 %
0.1 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
114
spark_second
code
89.53 %
91.23 %
85.02 %
. s
1 core @ 2.5 Ghz (Python)
115
spark_pointpillar
code
89.51 %
93.58 %
85.03 %
0.02 s
GPU @ 2.5 Ghz (Python)
116
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.
117
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.
118
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.
119
KPTr
89.48 %
92.74 %
84.50 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
120
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.
121
PA3DNet
89.46 %
93.11 %
84.60 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with
Pseudo Shape Segmentation and Adaptive Camera-
LiDAR Fusion . IEEE Transactions on Industrial
Informatics 2023.
122
PG-RCNN
code
89.46 %
93.39 %
86.54 %
0.06 s
GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point
Generation for 3D Object Detection . 2023.
123
DFAF3D
89.45 %
93.14 %
84.22 %
0.05 s
1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free
single-stage 3D detector for point clouds . Image and Vision Computing 2023.
124
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 . WACV 2023.
125
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.
126
R2Pfusion-Det
89.37 %
92.96 %
86.70 %
0.3 s
1 core @ 2.5 Ghz (C/C++)
127
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.
128
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.
129
GSG-FPS
code
89.32 %
92.77 %
84.27 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
130
CAIA_PRO
code
89.27 %
92.84 %
84.26 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
131
spark_second2
89.27 %
90.94 %
84.85 %
10 s
1 core @ 2.5 Ghz (Python)
132
ACDet
code
89.21 %
92.87 %
85.80 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion
for LiDAR-based 3D Object Detection . 3DV 2022.
133
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 . WACV 2023.
134
Test_dif
code
89.20 %
92.69 %
84.23 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
135
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.
136
FIRM-Net
89.18 %
92.56 %
86.33 %
0.07 s
1 core @ 2.5 Ghz (Python)
137
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.
138
HMFI
code
89.17 %
93.04 %
86.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and
Interaction for 3D Object Detection . ECCV 2022.
139
sec_spark
code
89.16 %
90.89 %
84.84 %
0.03 s
GPU @ 2.5 Ghz (Python)
140
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.
141
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.
142
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.
143
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.
144
RAFDet
89.05 %
92.29 %
84.35 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
145
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.
146
RagNet3D
code
89.01 %
92.87 %
86.36 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection . Available at SSRN 4979473 .
147
EPNet++
89.00 %
95.41 %
85.73 %
0.1 s
GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for
Multi-Modal 3D Object Detection . IEEE Transactions on
Pattern Analysis and Machine Intelligence 2022.
148
DDF
89.00 %
92.57 %
86.50 %
0.1 s
1 core @ 2.5 Ghz (Python)
149
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.
150
RAFDet
code
88.99 %
92.23 %
84.21 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
151
LGNet-Car
code
88.98 %
92.83 %
86.26 %
0.11 s
1 core @ 2.5 Ghz (Python + C/C++)
152
USVLab BSAODet
code
88.90 %
92.66 %
86.23 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective
for Single-Model Multi-Class 3D Object Detection . IEEE Transactions on Circuits and
Systems for Video Technology 2023.
153
bs
88.88 %
94.53 %
86.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
154
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.
155
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.
156
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.
157
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.
158
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.
159
GD-MAE
88.82 %
94.22 %
83.54 %
0.07 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre-
training on LiDAR Point Clouds . CVPR 2023.
160
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.
161
MG
88.66 %
92.64 %
83.61 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
162
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.
163
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.
164
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.
165
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.
166
MFB3D
88.54 %
94.67 %
85.75 %
0.14 s
1 core @ 2.5 Ghz (Python)
167
second_iou_baseline
code
88.48 %
92.24 %
85.57 %
0.05 s
1 core @ 2.5 Ghz (Python)
168
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.
169
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.
170
FARP-Net
code
88.45 %
91.20 %
86.01 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature
Aggregation and Relation-Aware Proposals for 3D
Object Detection . IEEE Transactions on Multimedia 2023.
171
PUDet
88.42 %
92.68 %
83.70 %
0.3 s
GPU @ 2.5 Ghz (Python)
172
AFFN-Ga
88.41 %
92.49 %
85.89 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
173
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.
174
second_iou_baseline
88.40 %
92.12 %
85.54 %
0.03 s
1 core @ 2.5 Ghz (Python)
175
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.
176
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.
177
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.
178
PASS-PV-RCNN-Plus
88.37 %
92.17 %
85.75 %
1 s
1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object
Detection via Point Assisted Sample Selection . will submit to computer vision
conference/journal 2024.
179
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.
180
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.
181
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.
182
SRDL
88.17 %
92.01 %
85.43 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
183
Res3DNet
88.16 %
91.71 %
84.85 %
0.05 s
GPU @ 3.5 Ghz (Python)
184
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.
185
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.
186
PartA2_basline
code
88.09 %
92.35 %
85.42 %
0.09 s
1 core @ 2.5 Ghz (Python)
187
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.
188
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.
189
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.
190
SFEBEV
88.08 %
93.44 %
83.01 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
191
pointpillar_spark_fo
88.02 %
92.48 %
84.82 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
192
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.
193
spark_pointpillar2
87.93 %
92.74 %
84.70 %
10 s
1 core @ 2.5 Ghz (Python)
194
BAPartA2S-4h
87.89 %
91.96 %
83.31 %
0.1 s
1 core @ 2.5 Ghz (Python)
195
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.
196
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.
197
SIF
87.76 %
91.44 %
85.15 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
P. An: SIF . Submitted to CVIU 2021.
198
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.
199
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.
200
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.
201
RangeDet (Official)
code
87.67 %
90.93 %
82.92 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range
View for LiDAR-Based 3D Object Detection . Proceedings of the IEEE/CVF
International Conference on Computer Vision
(ICCV) 2021.
202
pointpillar_baseline
code
87.61 %
92.52 %
83.84 %
0.01 s
1 core @ 2.5 Ghz (Python)
203
Second_baseline
code
87.60 %
90.94 %
84.36 %
0.03 s
1 core @ 2.5 Ghz (Python)
204
VoxelFSD-S
87.60 %
90.94 %
84.11 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
205
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.
206
TF-PartA2
87.54 %
91.93 %
83.33 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
207
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.
208
mm3d_PartA2
87.51 %
91.75 %
83.01 %
0.1 s
GPU @ >3.5 Ghz (Python)
209
SeSame-point
code
87.49 %
90.84 %
83.77 %
N/A s
TITAN RTX @ 1.35 Ghz (Python)
210
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.
211
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.
212
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.
213
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.
214
Sem-Aug
87.37 %
93.35 %
82.43 %
0.1 s
GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving
Camera-LiDAR Feature Fusion With Semantic
Augmentation for 3D Vehicle Detection . IEEE Robotics
and Automation Letters 2022.
215
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.
216
Harmonic PointPillar
code
87.28 %
90.89 %
82.54 %
0.01 s
1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent
and Time-friendly 3D Object Detection for V2X
Orchestration . will submit to IEEE Transactions on
Vehicular Technology 2022.
217
PASS-PointPillar
87.23 %
91.07 %
81.98 %
1 s
1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object
Detection via Point Assisted Sample Selection . will submit to computer vision conference/journal 2024.
218
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.
219
PCNet3D_Extension_V1
87.17 %
90.13 %
83.10 %
0.5 s
GPU @ 3.5 Ghz (Python)
220
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.
221
LVFSD
87.12 %
90.42 %
83.91 %
0.06 s
ERROR: Wrong syntax in BIBTEX file.
222
XT-PartA2
87.08 %
90.89 %
82.70 %
0.1 s
GPU @ >3.5 Ghz (Python)
223
centerpoint_pcdet
87.04 %
90.04 %
83.32 %
0.06 s
1 core @ 2.5 Ghz (Python)
224
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.
225
SeSame-pillar
code
86.88 %
90.61 %
81.93 %
N/A s
TITAN RTX @ 1.35 Ghz (Python)
226
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.
227
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.
228
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.
229
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.
230
PCNet3D
86.54 %
90.09 %
81.43 %
0.05 s
GPU @ 3.5 Ghz (Python)
231
T-SSD
86.50 %
92.50 %
81.30 %
0.04
1 core @ 2.0 Ghz (C/C++)
232
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.
233
MM_SECOND
code
86.39 %
90.52 %
81.49 %
0.05 s
GPU @ >3.5 Ghz (Python)
234
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.
235
DensePointPillars
86.31 %
92.13 %
81.12 %
0.03 s
GPU @ 2.5 Ghz (Python)
236
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.
237
VSAC
86.22 %
91.98 %
81.50 %
0.07 s
1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
238
voxelnext_pcdet
86.15 %
89.72 %
82.34 %
0.05 s
1 core @ 2.5 Ghz (Python)
239
SeSame-pillar w/scor
code
86.11 %
90.43 %
81.38 %
N/A s
1 core @ 2.5 Ghz (C/C++)
240
R50_SACINet
86.10 %
91.70 %
83.15 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
241
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.
242
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.
243
HINTED
code
86.01 %
90.61 %
79.29 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector
with Mixed-Density Feature Fusion for Sparsely-
Supervised 3D Object Detection . Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern
Recognition 2024.
244
L_SACINet
85.99 %
91.21 %
81.05 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
245
PL++: PV-RCNN++
85.89 %
91.76 %
81.29 %
0.342 s
RTX 4060Ti (Python)
246
Sem-Aug-PointRCNN++
85.88 %
91.68 %
83.37 %
0.1 s
8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving
Camera-LiDAR Feature Fusion With Semantic
Augmentation for 3D Vehicle Detection . IEEE Robotics
and Automation Letters 2022.
247
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.
248
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.
249
SecAtten
85.84 %
91.32 %
82.57 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
250
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.
251
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.
252
SeSame-voxel
code
85.62 %
89.86 %
80.95 %
N/A s
TITAN RTX @ 1.35 Ghz (Python)
253
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.
254
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.
255
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.
256
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.
257
PI-SECOND
code
84.83 %
90.15 %
79.86 %
0.05 s
GPU @ >3.5 Ghz (Python + C/C++)
258
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.
259
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.
260
AEPF
84.63 %
89.99 %
80.02 %
0.05 s
GPU @ 2.5 Ghz (Python)
261
mmFUSION
code
84.60 %
90.35 %
79.82 %
1s
1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects
Detection . arXiv preprint arXiv:2311.04058 2023.
262
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.
263
EOTL
code
83.14 %
89.10 %
71.41 %
TBD s
1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road
Participants Detection in Autonomous Driving . IEEE Sensors Journal 2023.
264
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.
265
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.
266
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.
267
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.
268
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.
269
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 . IEEE Transactions on Pattern Analysis and
Machine Intelligence 2022.
270
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.
271
StereoDistill
78.59 %
89.03 %
69.34 %
0.4 s
1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection . Proceedings of the AAAI Conference on Artificial Intelligence 2023.
272
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.
273
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.
274
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.
275
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.
276
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.
277
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.
278
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.
279
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.
280
SeSame-point w/score
code
67.18 %
83.44 %
57.68 %
N/A s
1 core @ 1.5 Ghz (Python)
281
SeSame-point w/score
code
67.18 %
83.44 %
57.68 %
N/A s
GPU @ 1.5 Ghz (Python)
282
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.
283
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.
284
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.
285
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.
286
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.
287
SeSame-voxel w/score
code
63.36 %
71.98 %
57.52 %
N/A s
GPU @ 1.5 Ghz (Python)
288
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.
289
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.
290
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.
291
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.
292
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.
293
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.
294
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.
295
ESGN
58.12 %
78.10 %
49.28 %
0.06 s
GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network
for Fast 3D Object Detection . IEEE Transactions on Circuits and
Systems for Video Technology 2022.
296
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.
297
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.
298
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.
299
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.
300
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.
301
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.
302
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.
303
SST [st]
47.07 %
71.08 %
41.90 %
1 s
1 core @ 2.5 Ghz (Python)
304
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.
305
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.
306
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.
307
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.
308
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.
309
DA3D+KM3D+v2-99
34.88 %
44.27 %
30.29 %
0.120s
GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies . IEEE Transactions on Instrumentation and Measurement 2024.
310
CIE + DM3D
33.13 %
46.17 %
28.80 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit
Features Matters for Monocular 3D Object
Detection . arXiv preprint arXiv:2207.07933 2022.
311
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.
312
MonoTAKD V2
32.31 %
43.83 %
28.48 %
0.1 s
1 core @ 2.5 Ghz (Python)
313
monodetrnext-a
30.68 %
37.32 %
31.29 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
314
RD3D
28.92 %
42.67 %
25.89 %
0.1 s
1 core @ 2.5 Ghz (Python)
315
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.
316
DA3D+KM3D
code
28.71 %
39.50 %
25.20 %
0.02 s
GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies . IEEE Transactions on Instrumentation and Measurement 2024.
317
CIE
28.50 %
41.41 %
23.88 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit
Features Matters for Monocular 3D Object
Detection . arXiv preprint arXiv:2207.07933 2022.
318
monodetrnext-f
28.12 %
34.56 %
28.33 %
0.03 s
GPU @ 2.5 Ghz (Python)
319
MonoMH
code
28.06 %
37.85 %
24.53 %
0.04 s
1 core @ 2.5 Ghz (Python)
320
MonoTAKD
27.76 %
38.75 %
24.14 %
0.1 s
1 core @ 2.5 Ghz (Python)
321
zqd
27.11 %
41.72 %
23.36 %
0.1 s
1 core @ 2.5 Ghz (Python)
322
DA3D
26.92 %
36.83 %
23.41 %
0.03 s
1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies . IEEE Transactions on Instrumentation and Measurement 2024.
323
MonoLiG
code
26.83 %
35.73 %
24.24 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi
Supervised Active Learning . 2023.
324
zqd_test2
26.21 %
41.36 %
22.64 %
0.5 s
1 core @ 2.5 Ghz (C/C++)
325
Sample
code
26.21 %
35.31 %
22.28 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
326
MonoLSS
25.95 %
34.89 %
22.59 %
0.04 s
1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For
Monocular 3D Detection . International Conference on 3D Vision 2024.
327
MonoAFKD
25.83 %
34.57 %
22.47 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
328
CMKD
code
25.82 %
38.98 %
22.80 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge
Distillation Network for Monocular 3D Object
Detection . ECCV 2022.
329
Occlude3D
code
25.41 %
33.08 %
20.75 %
0.01 s
1 core @ 2.5 Ghz (Python)
330
SH3D
25.25 %
35.64 %
22.09 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
331
BEVHeight++
code
24.90 %
37.51 %
20.93 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
L. Yang, T. Tang, J. Li, P. Chen, K. Yuan, L. Wang, Y. Huang, X. Zhang and K. Yu: Bevheight++: Toward robust visual centric
3d object detection . arXiv preprint arXiv:2309.16179 2023.
332
PS-SVDM
24.82 %
38.18 %
20.89 %
1 s
1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for
Pseudo-Stereo 3D Object Detection . arXiv preprint arXiv:2307.02270 2023.
333
LPCG-Monoflex
code
24.81 %
35.96 %
21.86 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D
Object Detection . ECCV 2022.
334
NeurOCS
24.49 %
37.27 %
20.89 %
0.1 s
GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision
for Monocular 3D Object Localization . CVPR 2023.
335
Mix-Teaching
code
24.23 %
35.74 %
20.80 %
30 s
1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and
Effective Semi-Supervised Learning Framework for
Monocular 3D Object Detection . ArXiv 2022.
336
MonoSKD
code
24.08 %
37.12 %
20.37 %
0.04 s
1 core @ 2.5 Ghz (Python)
S. Wang and J. Zheng: MonoSKD: General Distillation Framework for
Monocular 3D Object Detection via Spearman
Correlation Coefficient . ECAI 2023.
337
MonoSample (DID-M3D)
code
23.94 %
37.64 %
20.46 %
0.2 s
1 core @ 2.5 Ghz (C/C++)
J. Qiao, B. Liu, J. Yang, B. Wang, S. Xiu, X. Du and X. Nie: MonoSample: Synthetic 3D Data
Augmentation Method in Monocular 3D Object
Detection . IEEE Robotics and Automation Letters 2024.
338
TBD
23.87 %
37.10 %
20.24 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
339
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.
340
MSFENet
code
23.65 %
36.81 %
20.06 %
0.1 s
1 core @ 2.5 Ghz (Python)
341
SHUD
23.63 %
36.39 %
20.01 %
0.04 s
1 core @ 2.5 Ghz (Python)
342
ADD
code
23.58 %
35.20 %
20.08 %
0.1 s
1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional
Encoding for Monocular 3D Object Detection . AAAI2023 .
343
MonoNeRD
code
23.46 %
31.13 %
20.97 %
na s
1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for
Monocular 3D Object Detection . ICCV 2023.
344
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.
345
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) .
346
MonoSGC
23.27 %
35.78 %
19.92 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
347
FDGNet
code
23.27 %
36.25 %
19.56 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
348
MonoUNI
code
23.05 %
33.28 %
19.39 %
0.04 s
1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and
Infrastructure-side Monocular 3D Object Detection
Network with Sufficient Depth Clues . Thirty-seventh Conference on Neural
Information Processing Systems 2023.
349
zqd_test
23.00 %
35.01 %
20.99 %
0.2 s
1 core @ 2.5 Ghz (Python)
350
LLW
22.86 %
38.51 %
19.26 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
351
MonoCD
code
22.81 %
33.41 %
19.57 %
n/a s
1 core @ 2.5 Ghz (Python)
L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with
Complementary Depths . CVPR 2024.
352
MonoFRD
22.77 %
29.65 %
20.41 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
353
DID-M3D
code
22.76 %
32.95 %
19.83 %
0.04 s
1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for
Monocular 3D Object Detection . ECCV 2022.
354
OPA-3D
code
22.53 %
33.54 %
19.22 %
0.04 s
1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise
Aggregation for Monocular 3D Object Detection . IEEE Robotics and Automation Letters 2023.
355
DCD
code
21.50 %
32.55 %
18.25 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for
Monocular 3D Object Detection . European Conference on Computer
Vision 2022.
356
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.
357
SGM3D
code
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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object
Detection . RA-L 2022.
358
Cube R-CNN
code
21.20 %
31.70 %
18.43 %
0.05 s
GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and
Model for 3D Object Detection in the Wild . CVPR 2023.
359
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.
360
MonoSIM_v2
21.19 %
30.36 %
18.45 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
361
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.
362
DEVIANT
code
20.44 %
29.65 %
17.43 %
0.04 s
1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection . European Conference on Computer Vision (ECCV) 2022.
363
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.
364
MDSNet
20.14 %
32.81 %
15.77 %
0.05 s
1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification
3D Object Detection from Monocular Images . Sensors 2022.
365
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.
366
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.
367
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.
368
MonOAPC
19.67 %
28.91 %
16.99 %
0035 s
1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for
Monocular 3D Object Detection . IEEE Transactions on Intelligent
Transportation Systems 2023.
369
MonoDSSMs-M
19.59 %
28.29 %
16.34 %
0.02 s
1 core @ 2.5 Ghz (Python + C/C++)
D. Kiet Dang Vu: MonoDSSMs: Efficient Monocular 3D Object
Detection with Depth-Aware State Space Models . Computer Vision - ACCV 2024 - 17th
Asian Conference on Computer Vision, HaNoi,
VietNam, December 8-10, 2024, Proceedings 2024.
370
MonoDSSMs-A
19.54 %
28.84 %
16.30 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
D. Kiet Dang Vu: MonoDSSMs: Efficient Monocular 3D Object
Detection with Depth-Aware State Space Models . Computer Vision - ACCV 2024 - 17th
Asian Conference on Computer Vision, HaNoi,
VietNam, December 8-10, 2024, Proceedings 2024.
371
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.
372
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.
373
PS-SVDM
19.07 %
28.52 %
16.30 %
1 s
1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for
Pseudo-Stereo 3D Object Detection . arXiv preprint arXiv:2307.02270 2023.
374
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.
375
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.
376
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.
377
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 .
378
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.
379
MonoRCNN++
code
18.62 %
27.20 %
15.69 %
0.07 s
GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D
Object Detection . WACV 2023.
380
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.
381
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.
382
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.
383
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.
384
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.
385
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.
386
mdab
17.74 %
26.42 %
15.71 %
22 s
1 core @ 2.5 Ghz (C/C++)
387
FMF-occlusion-net
17.60 %
27.39 %
13.25 %
0.16 s
1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti-
occlusion Monocular 3D Object Detection . IEEE Transactions on Image Processing 2022.
388
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.
389
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 .
390
MonoAuxNorm
17.38 %
23.43 %
14.74 %
0.02 s
GPU @ 2.5 Ghz (Python)
391
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.
392
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.
393
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.
394
CMAN
17.04 %
25.89 %
12.88 %
0.15 s
1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation
for
Monocular 3D Object Detection . IEEE Trans. Intell. Transport. Syst. 2022.
395
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.
396
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.
397
SAKD-MR-Res18
16.56 %
26.48 %
13.67 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
398
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.
399
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.
400
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.
401
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.
402
mdab
15.09 %
23.18 %
13.38 %
22 s
1 core @ 2.5 Ghz (Python)
403
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.
404
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.
405
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.
406
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.
407
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.
408
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.
409
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 .
410
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.
411
mdab
12.67 %
18.79 %
10.41 %
0.02 s
1 core @ 2.5 Ghz (Python)
412
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.
413
Plane-Constraints
code
12.06 %
17.31 %
10.05 %
0.05 s
4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular
3D object detection via intra-and inter-plane
constraints . Neural Networks 2023.
414
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.
415
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.
416
mdab
11.47 %
17.81 %
9.08 %
0.02 s
1 core @ 2.5 Ghz (Python)
417
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.
418
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.
419
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.
420
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.
421
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.
422
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.
423
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.
424
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.
425
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.
426
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.
427
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.
428
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.
429
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.
430
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.
431
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 .
432
f3sd
code
0.01 %
0.00 %
0.01 %
1.67 s
1 core @ 2.5 Ghz (C/C++)
433
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.
434
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.