Method
Setting
Code
Moderate Easy
Hard
Runtime
Environment
1
VirConv-S code 87.20 % 92.48 %
82.45 %
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 86.72 %
91.65 %
82.59 %
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 86.25 %
92.54 % 81.24 %
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
HPC-Net 85.50 %
92.08 %
82.65 % 0.18 s
1 core @ 2.5 Ghz (C/C++)
5
TSSTDet 85.47 %
91.84 %
80.65 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
6
3ONet 85.47 %
92.03 %
78.64 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
7
TED code 85.28 %
91.61 %
80.68 %
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
LoGoNet code 85.06 %
91.80 %
80.74 %
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.
9
Simi-fusion 85.02 %
91.82 %
82.43 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
10
LVP(84.92) 84.92 %
91.37 %
80.07 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
11
CDF 84.87 %
91.02 %
79.41 %
0.08 s
1 core @ 2.5 Ghz (C/C++)
12
SFD code 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.
13
FEIF3D 84.56 %
91.27 %
80.05 %
0.1 s
GPU @ 2.5 Ghz (Python)
14
HAF-PVP_test 84.50 %
91.53 %
77.85 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
15
LVP 84.49 %
90.90 %
79.39 %
0.04 s
1 core @ 2.5 Ghz (Python)
16
Anonymous 84.40 %
91.31 %
80.04 %
0.1 s
1 core @ 2.5 Ghz (Python)
17
SA-Net 84.39 %
91.37 %
77.61 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
18
TED-S Reproduced 84.29 %
91.62 %
80.00 %
0.1 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
19
VDF 84.25 %
91.39 %
78.96 %
0.03 s
GPU @ 2.5 Ghz (Python)
20
3D HANet code 84.18 %
90.79 %
77.57 %
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.
21
CasA++ code 84.04 %
90.68 %
79.69 %
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.
22
Anonymous 83.98 %
91.14 %
79.62 %
0.1 s
GPU @ 2.5 Ghz (Python)
23
CASDC 83.95 %
88.50 %
77.21 %
0.1 s
1 core @ 2.5 Ghz (Python)
24
L-AUG 83.84 %
90.53 %
79.10 %
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.
25
URFormer 83.40 %
89.64 %
78.62 %
0.1 s
1 core @ 2.5 Ghz (Python)
26
MSIT-Det 83.27 %
92.11 %
73.81 %
0.06 s
1 core @ 2.5 Ghz (Python + C/C++)
27
GraR-VoI code 83.27 %
91.89 %
77.78 %
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.
28
SoRL-V 83.24 %
90.07 %
76.37 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
29
GLENet-VR code 83.23 %
91.67 %
78.43 %
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.
30
VPFNet code 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 . IEEE Transactions on Multimedia 2022.
31
GraR-Po code 83.18 %
91.79 %
77.98 %
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.
32
IMLIDAR(base) 83.16 %
91.50 %
78.32 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
33
CasA code 83.06 %
91.58 %
80.08 %
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.
34
SoRL 82.95 %
91.83 %
78.12 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
35
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.
36
DAF-SSD 82.86 %
91.77 %
75.35 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
37
GraR-Vo code 82.77 %
91.29 %
77.20 %
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.
38
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.
39
OcTr 82.64 %
90.88 %
77.77 %
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.
40
MAK_VOXEL_RCNN 82.62 %
91.29 %
77.93 %
0.03 s
1 core @ 2.5 Ghz (Python)
41
IA-SSDx 82.61 %
89.31 %
77.91 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
42
casx 82.61 %
89.31 %
77.91 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
43
PA3DNet 82.57 %
90.49 %
77.88 %
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.
44
casxv1 82.54 %
89.11 %
77.77 %
0.01 s
1 core @ 2.5 Ghz (Python)
45
POP-RCNN 82.54 %
91.02 %
77.76 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
46
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.
47
IIOU code 82.51 %
88.83 %
75.82 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
48
MMF 82.50 %
89.05 %
77.59 %
1 s
1 core @ 2.5 Ghz (C/C++)
49
MAK code 82.50 %
88.97 %
77.81 %
0.03 s
GPU @ 2.5 Ghz (Python)
50
PIPC-3Ddet code 82.46 %
91.65 %
77.60 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
51
Anonymous 82.46 %
89.10 %
77.78 %
0.1 s
GPU @ 2.5 Ghz (Python)
52
SFD++ 82.46 %
90.93 %
77.27 %
0.12 s
1 core @ 2.5 Ghz (Python + C/C++)
53
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 . WACV 2023.
54
PA-RCNN code 82.44 %
90.94 %
77.69 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
55
GraR-Pi code 82.42 %
90.94 %
77.00 %
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.
56
R^2 R-CNN 82.42 %
90.93 %
77.84 %
0.1 s
1 core @ 2.5 Ghz (Python)
57
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 . WACV 2023.
58
3D Dual-Fusion code 82.40 %
91.01 %
79.39 %
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.
59
RDIoU code 82.30 %
90.65 %
77.26 %
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.
60
PVT-SSD 82.29 %
90.65 %
76.85 %
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.
61
DA-Net 82.29 %
90.73 %
77.90 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
62
Anonymous 82.29 %
88.40 %
77.63 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
63
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.
64
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.
65
Anonymous 82.24 %
88.83 %
77.62 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
66
GraphAlign 82.23 %
90.90 %
79.67 %
0.03 s
GPU @ 2.0 Ghz (Python)
67
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.
68
RPF3D 82.16 %
88.81 %
77.48 %
0.1 s
1 core @ 2.5 Ghz (Python)
69
TEDx 82.16 %
88.58 %
79.12 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
70
LGSL 82.16 %
90.51 %
79.33 %
0.1 s
GPU @ 2.5 Ghz (Python)
71
PG-RCNN code 82.13 %
89.38 %
77.33 %
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.
72
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.
73
FEMV-RCNN code 82.11 %
88.77 %
77.65 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
74
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.
75
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.
76
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.
77
Multi-Weights 82.05 %
90.06 %
77.36 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
78
Under Blind Review#1 82.04 %
88.45 %
77.54 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
79
PSMS-Net 82.02 %
90.88 %
77.37 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
80
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.
81
GS-FPS code 82.00 %
88.78 %
77.08 %
TBD s
1 core @ 2.5 Ghz (C/C++)
82
EPNet++ 81.96 %
91.37 %
76.71 %
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.
83
USVLab BSAODet code 81.95 %
88.66 %
77.40 %
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.
84
NIV-SSD 81.95 %
90.98 %
76.83 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
85
HMFI code 81.93 %
88.90 %
77.30 %
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.
86
PA-Det3D 81.92 %
88.46 %
77.38 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
87
3D-BCM 81.91 %
90.36 %
77.01 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
88
BSH-Det3D 81.91 %
88.75 %
77.36 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
89
AMVFNet code 81.90 %
90.52 %
77.42 %
0.04 s
GPU @ 2.5 Ghz (Python)
90
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.
91
af 81.78 %
90.46 %
77.37 %
1 s
GPU @ 2.5 Ghz (Python)
92
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.
93
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.
94
DSASA code 81.72 %
88.64 %
76.73 %
0.05s
GPU @ >3.5 Ghz (Python)
95
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.
96
F3D 81.69 %
88.40 %
77.09 %
0.01 s
1 core @ 2.5 Ghz (C/C++)
97
DCAF code 81.63 %
91.04 %
76.68 %
0.12 s
1 core @ 2.5 Ghz (C/C++)
98
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.
99
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.
100
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.
101
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.
102
FARP-Net code 81.53 %
88.36 %
78.98 %
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.
103
CAFI-Pillars 81.53 %
87.66 %
77.00 %
30ms
NVIDIA Tesla P40 GPU
104
GENet 81.50 %
87.32 %
77.00 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
105
PR-SSD 81.49 %
89.69 %
76.71 %
0.02 s
GPU @ 2.5 Ghz (Python)
106
HybridPillars 81.48 %
90.06 %
76.94 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
107
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.
108
MPFusion 81.46 %
87.71 %
76.84 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
109
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.
110
GA-RCNN 81.44 %
87.96 %
77.05 %
47ms
1 core @ 2.5 Ghz (C/C++)
111
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.
112
Rnet 81.41 %
88.62 %
76.94 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
113
CZY_PPF_Net 81.39 %
90.44 %
77.02 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
114
SGFNet 81.39 %
88.80 %
76.47 %
0.09 s
1 core @ 2.5 Ghz (C/C++)
115
PV-PMRTNet 81.39 %
90.66 %
76.93 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
116
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.
117
SS-3DSSD code 81.35 %
87.98 %
76.43 %
0.014s
1 core @ 2.5 Ghz (C/C++)
118
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.
119
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.
120
GF-pointnet 81.23 %
88.23 %
76.53 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
121
GeVo 81.11 %
89.34 %
76.69 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
122
HA-PillarNet 81.06 %
89.65 %
76.67 %
0.05 s
1 core @ 2.5 Ghz (Python)
123
MVENet 81.03 %
87.75 %
76.52 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
124
U_PV_V2_ep_100_100 80.97 %
87.41 %
76.58 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
125
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.
126
RBEV-Voxel code 80.87 %
87.15 %
76.28 %
0.08 s
GPU @ 2.5 Ghz (Python)
127
Sem-Aug 80.77 %
89.41 %
75.90 %
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.
128
U_PV_V2_ep100_80 80.75 %
87.16 %
76.43 %
0... s
1 core @ 2.5 Ghz (Python)
129
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.
130
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.
131
HPV-RCNN 80.61 %
89.33 %
75.53 %
0.08 s
1 core @ 2.5 Ghz (Python)
132
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.
133
SRDL 80.38 %
87.73 %
76.27 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
134
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.
135
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.
136
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.
137
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.
138
PVTr 80.19 %
86.96 %
75.97 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
139
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.
140
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.
141
3D-CVF at SPA code 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.
142
STNet 80.01 %
87.02 %
75.76 %
0.60 s
1 core @ 2.5 Ghz (Python)
143
IOUFusion 80.00 %
87.72 %
75.00 %
0.1 s
GPU @ 2.5 Ghz (Python)
144
fuf 79.98 %
89.44 %
74.65 %
10 s
1 core @ 2.5 Ghz (C/C++)
145
DTSSD 79.94 %
88.62 %
74.91 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
146
SIF 79.88 %
86.84 %
75.89 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
P. An: SIF . Submitted to CVIU 2021.
147
DTSSD 79.85 %
88.15 %
74.81 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
148
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.
149
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.
150
MMAE 79.75 %
88.07 %
74.41 %
0.07 s
1 core @ 2.5 Ghz (C/C++)
151
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.
152
ACCF 79.69 %
87.69 %
74.75 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
153
OFFNet 79.68 %
85.81 %
75.41 %
0.1 s
GPU @ 2.5 Ghz (Python)
154
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.
155
B2PE 79.66 %
87.86 %
74.54 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
156
RAFDet 79.66 %
88.17 %
74.75 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
157
TADP 79.65 %
88.93 %
74.17 %
0.04 s
GPU @ 2.5 Ghz (Python)
158
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.
159
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.
160
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.
161
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.
162
Point-GNN_GT code 79.47 %
88.33 %
72.30 %
5 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
163
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.
164
DFAF3D 79.37 %
88.59 %
72.21 %
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.
165
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.
166
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.
167
PEF code 79.26 %
88.33 %
74.51 %
N/A s
1 core @ 2.5 Ghz (C/C++)
168
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.
169
AAMVFNet code 79.09 %
88.01 %
76.43 %
0.04 s
GPU @ 2.5 Ghz (Python)
170
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.
171
U_VOXELRCNN_CAR_V3 79.04 %
86.96 %
75.86 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
172
GD-MAE 79.03 %
88.14 %
73.55 %
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.
173
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.
174
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.
175
ACDet code 78.85 %
88.47 %
73.86 %
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.
176
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.
177
VPNetv2 78.70 %
87.36 %
74.10 %
0.1 s
1 core @ 2.5 Ghz (Python)
178
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.
179
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.
180
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.
181
RealSynthesis-SECOND 78.38 %
86.78 %
74.67 %
0.05 s
1 core @ 2.5 Ghz (Python)
182
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.
183
Sem-Aug-PointRCNN++ 78.06 %
86.69 %
73.85 %
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.
184
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.
185
HA-SSD 77.52 %
85.51 %
74.02 %
1 s
1 core @ 2.5 Ghz (C/C++)
186
U_second_v4_ep_100_8 77.45 %
85.20 %
73.84 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
187
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.
188
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.
189
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.
190
RangeDet (Official) code 77.36 %
85.41 %
72.60 %
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.
191
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.
192
u_second_v4_epoch_10 76.99 %
84.23 %
73.75 %
0.1 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
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.
195
VSAC 76.64 %
86.48 %
72.04 %
0.07 s
1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
196
IIOU_LDR code 76.51 %
86.95 %
71.53 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
197
OA-TSSD 76.50 %
83.34 %
73.42 %
20 s
8 cores @ 2.5 Ghz (C/C++)
198
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.
199
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.
200
ROT_S3D 76.35 %
86.56 %
71.51 %
0.1 s
GPU @ 2.5 Ghz (Python)
201
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.
202
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.
203
prcnn_v18_80_100 76.03 %
84.37 %
71.44 %
0.1 s
1 core @ 2.5 Ghz (Python)
204
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.
205
MMpointpillars 75.75 %
85.86 %
70.65 %
0.05 s
1 core @ 2.5 Ghz (Python)
206
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.
207
HybridPillars (SSD) 75.72 %
86.22 %
70.48 %
0.02 s
1 core @ 2.5 Ghz (C/C++)
208
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.
209
MSAW 75.57 %
84.46 %
68.87 %
0.42 s
2 cores @ 2.5 Ghz (Python)
210
MEDL-U 75.56 %
85.43 %
68.79 %
1 s
GPU @ >3.5 Ghz (Python)
211
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.
212
CAT2 75.33 %
84.84 %
70.07 %
1 s
1 core @ 2.5 Ghz (C/C++)
213
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.
214
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.
215
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.
216
HA PillarNet 74.89 %
83.83 %
70.11 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
217
PASS-PointPillar 74.85 %
84.72 %
69.05 %
1 s
1 core @ 2.5 Ghz (C/C++)
218
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.
219
P2P code 74.76 %
85.47 %
67.96 %
0.1 s
GPU @ 2.5 Ghz (Python + C/C++)
220
MLAFF 74.64 %
83.75 %
68.32 %
0.39 s
2 cores @ 2.5 Ghz (Python)
221
WA 74.59 %
84.80 %
67.27 %
0.3 s
GPU @ 2.5 Ghz (Python + C/C++)
222
Anonymous Submission 74.38 %
85.24 %
69.43 %
1s
1 core @ 2.5 Ghz (Python)
223
MMpp 74.34 %
83.64 %
68.02 %
0.05 s
1 core @ 2.5 Ghz (C/C++)
224
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.
225
HINTED 74.13 %
84.00 %
67.03 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
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
Harmonic PointPillar code 73.96 %
82.26 %
69.21 %
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.
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
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.
232
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.
233
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.
234
Voxel-MAE+SECOND code 72.87 %
80.11 %
69.43 %
0.05 s
1 core @ 2.5 Ghz (Python + C/C++)
235
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.
236
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.
237
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.
238
DFSemONet(Baseline) 71.24 %
81.63 %
64.89 %
0.04 s
GPU @ 2.5 Ghz (Python)
239
MVAF-Net(3-classes) 70.88 %
82.89 %
64.47 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
240
ODGS 70.85 %
78.39 %
64.81 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
241
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.
242
APDM code 70.53 %
79.78 %
65.48 %
0.7 s
1 core @ 2.5 Ghz (Python)
243
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.
244
EOTL code 69.13 %
79.97 %
58.57 %
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.
245
MVAF-Net(3-classes) 69.07 %
81.66 %
64.00 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
246
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.
247
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.
248
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 . IEEE Transactions on Pattern Analysis and
Machine Intelligence 2022.
249
DMF 67.33 %
77.55 %
62.44 %
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.
250
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.
251
StereoDistill 66.39 %
81.66 %
57.39 %
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.
252
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.
253
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.
254
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.
255
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.
256
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.
257
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.
258
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.
259
PS++ 54.58 %
74.64 %
46.24 %
0.4 s
1 core @ 2.5 Ghz (C/C++)
260
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.
261
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.
262
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.
263
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.
264
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.
265
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.
266
ESGN 46.39 %
65.80 %
38.42 %
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.
267
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.
268
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.
269
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.
270
DSC3D 42.54 %
66.46 %
34.04 %
0.31 s
GPU @ 2.5 Ghz (Python)
271
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.
272
TS3D 41.29 %
64.61 %
30.68 %
0.09 s
GPU @ 1.5 Ghz (Python + C/C++)
273
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.
274
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.
275
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.
276
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.
277
BKDStereo3D code 35.23 %
59.38 %
25.24 %
0.1 s
1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
278
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.
279
BKDStereo3D w/o KD code 32.08 %
56.72 %
23.74 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
280
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.
281
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.
282
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.
283
CIE + DM3D 25.02 %
35.96 %
21.47 %
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.
284
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.
285
SVDM-VIEW 23.07 %
39.40 %
19.52 %
1 s
1 core @ 2.5 Ghz (Python)
286
MonoTRKDv2 21.87 %
30.26 %
18.87 %
40 s
1 core @ 2.5 Ghz (Python)
287
CIE 20.95 %
31.55 %
17.83 %
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.
288
DA3D 20.47 %
27.76 %
17.89 %
0.03 s
1 core @ 2.5 Ghz (Python)
289
Anonymous 19.65 %
29.84 %
16.41 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
290
MonoLTKD 19.43 %
27.91 %
16.51 %
0.04 s
1 core @ 2.5 Ghz (C/C++)
291
MonoLSS 19.15 %
26.11 %
16.94 %
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.
292
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.
293
MonoInsight 19.04 %
27.71 %
16.03 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
294
MonoInsight 19.04 %
27.71 %
16.03 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
295
NeurOCS 18.94 %
29.89 %
15.90 %
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.
296
MonoLiG code 18.86 %
24.90 %
16.79 %
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.
297
CMKD code 18.69 %
28.55 %
16.77 %
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.
298
MonoAux-v2 code 18.55 %
26.00 %
15.79 %
0.04 s
GPU @ 2.5 Ghz (Python)
299
Mix-Teaching code 18.54 %
26.89 %
15.79 %
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.
300
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.
301
Anonymous 18.19 %
25.48 %
14.27 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
302
3Dv2 18.12 %
26.80 %
15.42 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
303
LPCG-Monoflex code 17.80 %
25.56 %
15.38 %
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.
304
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.
305
MonoATT_V2 code 17.44 %
23.84 %
16.33 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
306
MonoSKD code 17.35 %
28.43 %
15.01 %
0.04 s
1 core @ 2.5 Ghz (Python)
307
MonoSTL 17.14 %
24.54 %
14.59 %
na s
1 core @ 2.5 Ghz (Python)
308
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.
309
MonoCDiT 17.13 %
23.52 %
14.37 %
0.05 s
GPU @ >3.5 Ghz (Python)
310
MonoNeRD code 17.13 %
22.75 %
15.63 %
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.
311
OPA-3D code 17.05 %
24.60 %
14.25 %
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.
312
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.
313
OccupancyM3D 17.02 %
25.55 %
14.79 %
0.11 s
1 core @ 2.5 Ghz (C/C++)
314
SHUD 16.97 %
26.93 %
14.82 %
0.04 s
1 core @ 2.5 Ghz (Python)
315
MonoAux 16.90 %
23.87 %
14.09 %
0.04 s
GPU @ 2.5 Ghz (Python)
316
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) .
317
ADD code 16.81 %
25.61 %
13.79 %
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 .
318
UNM3D 16.80 %
24.54 %
14.64 %
na s
1 core @ 2.5 Ghz (C/C++)
319
MonoUNI code 16.73 %
24.75 %
13.49 %
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.
320
MOPNet code 16.67 %
26.95 %
14.33 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
321
LWLANet code 16.67 %
26.74 %
14.33 %
0.1 s
1 core @ 2.5 Ghz (Python + C/C++)
322
MonoTRKD 16.66 %
25.61 %
13.74 %
40 s
1 core @ 2.5 Ghz (Python)
323
MonoCD 16.59 %
25.53 %
14.53 %
n/a s
1 core @ 2.5 Ghz (Python)
324
BA2-Det+MonoFlex 16.30 %
23.45 %
13.50 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
325
DID-M3D code 16.29 %
24.40 %
13.75 %
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.
326
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.
327
DCD code 15.90 %
23.81 %
13.21 %
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.
328
MM3DV2 15.66 %
23.18 %
14.08 %
NA s
1 core @ 2.5 Ghz (C/C++)
329
DE_Fusion 15.62 %
24.33 %
12.62 %
0.06 s
1 core @ 2.5 Ghz (C/C++)
330
MonoRoIDepth 15.51 %
23.62 %
12.43 %
1 s
1 core @ 2.5 Ghz (C/C++)
331
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.
332
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.
333
Cube R-CNN code 15.01 %
23.59 %
12.56 %
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.
334
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.
335
DD3D-dequity 14.76 %
21.71 %
12.92 %
0.1 s
1 core @ 2.5 Ghz (C/C++)
336
SGM3D code 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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object
Detection . RA-L 2022.
337
MDSNet 14.46 %
24.30 %
11.12 %
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.
338
DEVIANT code 14.46 %
21.88 %
11.89 %
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.
339
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.
340
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.
341
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.
342
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.
343
MonoRCNN++ code 13.72 %
20.08 %
11.34 %
0.07 s
GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D
Object Detection . WACV 2023.
344
MonoSIM 13.72 %
20.24 %
12.29 %
0.16 s
1 core @ 2.5 Ghz (Python)
345
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.
346
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.
347
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.
348
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.
349
FMF-occlusion-net 13.12 %
20.28 %
9.56 %
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.
350
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.
351
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.
352
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.
353
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 .
354
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.
355
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.
356
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.
357
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 .
358
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.
359
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.
360
MonOAPC 12.00 %
18.77 %
9.75 %
0035 s
1 core @ 2.5 Ghz (Python)
361
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.
362
CMAN 11.87 %
17.77 %
9.16 %
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.
363
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.
364
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.
365
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.
366
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.
367
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.
368
MonoAIU 11.02 %
15.73 %
8.82 %
0.03 s
GPU @ 2.5 Ghz (Python)
369
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.
370
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.
371
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.
372
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.
373
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.
374
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.
375
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 .
376
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.
377
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.
378
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.
379
Plane-Constraints 7.88 %
11.29 %
6.48 %
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.
380
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.
381
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.
382
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.
383
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.
384
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.
385
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.
386
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.
387
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.
388
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.
389
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.
390
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.
391
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.
392
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.
393
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.
394
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.
395
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.
396
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.
397
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.
398
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.
399
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.
400
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.
401
Initial_submit code 0.03 %
0.05 %
0.04 %
0.03 s
1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
402
1D_CONV 0.00 %
0.00 %
0.00 %
0.03 s
1 core @ 2.5 Ghz (Python)
403
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.
Stereo: Method uses left and right (stereo) images
Flow: Method uses optical flow (2 temporally adjacent images)
Multiview: Method uses more than 2 temporally adjacent images
Laser Points: Method uses point clouds from Velodyne laser scanner
Additional training data: Use of additional data sources for training (see details)