\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
LSFM & & 81.26 \% & 86.81 \% & 77.64 \% & 0.05 s / 4 cores & \\
F-PointNet & la & 80.13 \% & 89.83 \% & 75.05 \% & 0.17 s / GPU & 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.\\
HHA-TFFEM & la & 78.53 \% & 87.01 \% & 74.70 \% & 0.14 s / GPU & F. Tan, Z. Xia, Y. Ma and X. Feng: 3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion. Remote Sensing 2022.\\
TuSimple & & 78.40 \% & 88.87 \% & 73.66 \% & 1.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.\\
RRC & & 76.61 \% & 85.98 \% & 71.47 \% & 3.6 s / GPU & J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.\\
WSSN & la & 76.42 \% & 84.91 \% & 71.86 \% & 0.37 s / GPU & Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.\\
ECP Faster R-CNN & & 76.25 \% & 85.96 \% & 70.55 \% & 0.25 s / GPU & M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.\\
Aston-EAS & & 76.07 \% & 86.71 \% & 70.02 \% & 0.24 s / GPU & J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.\\
MHN & & 75.99 \% & 87.21 \% & 69.50 \% & 0.39 s / GPU & J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.\\
FFNet & & 75.81 \% & 87.17 \% & 69.86 \% & 1.07 s / GPU & C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.\\
SJTU-HW & & 75.81 \% & 87.17 \% & 69.86 \% & 0.85s / GPU & S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.\\
MS-CNN & & 74.89 \% & 85.71 \% & 68.99 \% & 0.4 s / GPU & Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.\\
DD3D & & 73.09 \% & 85.71 \% & 68.54 \% & n/a s / 1 core & 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) .\\
F-ConvNet & la & 72.91 \% & 83.63 \% & 67.18 \% & 0.47 s / GPU & Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.\\
GN & & 72.29 \% & 82.93 \% & 65.56 \% & 1 s / GPU & S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.\\
SubCNN & & 72.27 \% & 84.88 \% & 66.82 \% & 2 s / GPU & Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.\\
VMVS & la & 71.82 \% & 82.80 \% & 66.85 \% & 0.25 s / GPU & J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.\\
EOTL & & 71.45 \% & 84.74 \% & 64.58 \% & TBD s / 1 core & 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.\\
IVA & & 71.37 \% & 84.61 \% & 64.90 \% & 0.4 s / GPU & Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.\\
MM-MRFC & fl la & 70.76 \% & 83.79 \% & 64.81 \% & 0.05 s / GPU & A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.\\
SDP+RPN & & 70.42 \% & 82.07 \% & 65.09 \% & 0.4 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.\\
3DOP & st & 69.57 \% & 83.17 \% & 63.48 \% & 3s / GPU & X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.\\
MonoPSR & & 68.56 \% & 85.60 \% & 63.34 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
DeepStereoOP & & 68.46 \% & 83.00 \% & 63.35 \% & 3.4 s / GPU & C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.\\
sensekitti & & 68.41 \% & 82.72 \% & 62.72 \% & 4.5 s / GPU & B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.\\
MonoLSS & & 67.78 \% & 82.88 \% & 60.87 \% & 0.04 s / 1 core & Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.\\
Frustum-PointPillars & & 67.51 \% & 76.80 \% & 63.81 \% & 0.06 s / 4 cores & A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.\\
FII-CenterNet & & 67.31 \% & 81.32 \% & 61.29 \% & 0.09 s / GPU & S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.\\
Mono3D & & 67.29 \% & 80.30 \% & 62.23 \% & 4.2 s / GPU & X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.\\
Faster R-CNN & & 66.24 \% & 79.97 \% & 61.09 \% & 2 s / GPU & S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.\\
VPFNet & & 65.68 \% & 75.03 \% & 61.95 \% & 0.2 s / 1 core & 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.\\
UPIDet & & 65.50 \% & 75.07 \% & 63.09 \% & 0.11 s / 1 core & 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.\\
EQ-PVRCNN & & 65.01 \% & 77.19 \% & 61.95 \% & 0.2 s / GPU & 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.\\
CasA++ & & 64.94 \% & 74.41 \% & 62.35 \% & 0.1 s / 1 core & 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.\\
TED & & 64.74 \% & 74.26 \% & 62.08 \% & 0.1 s / 1 core & H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.\\
LoGoNet & & 64.55 \% & 72.47 \% & 62.24 \% & 0.1 s / 1 core & 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.\\
SDGUFusion & & 64.39 \% & 73.43 \% & 61.97 \% & 0.5 s / 1 core & \\
SDP+CRC (ft) & & 64.36 \% & 79.22 \% & 59.16 \% & 0.6 s / GPU & F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.\\
Pose-RCNN & & 63.54 \% & 80.07 \% & 57.02 \% & 2 s / >8 cores & M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.\\
USVLab BSAODet & & 63.21 \% & 72.86 \% & 59.48 \% & 0.04 s / 1 core & 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.\\
MLF-DET & & 63.09 \% & 70.25 \% & 59.23 \% & 0.09 s / 1 core & 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.\\
R^2 R-CNN & & 63.07 \% & 72.34 \% & 59.49 \% & 0.1 s / 1 core & \\
af & & 62.87 \% & 71.50 \% & 59.22 \% & 1 s / GPU & \\
CFM & & 62.84 \% & 74.76 \% & 56.06 \% & & Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.\\
CasA & & 62.73 \% & 72.65 \% & 60.12 \% & 0.1 s / 1 core & 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.\\
Fast-CLOCs & & 62.57 \% & 76.22 \% & 60.13 \% & 0.1 s / GPU & 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.\\
FIRM-Net & & 62.50 \% & 72.65 \% & 59.84 \% & 0.07 s / 1 core & \\
PiFeNet & & 62.35 \% & 72.74 \% & 59.29 \% & 0.03 s / 1 core & D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.\\
HotSpotNet & & 62.31 \% & 71.43 \% & 59.24 \% & 0.04 s / 1 core & Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
P2V-RCNN & & 61.83 \% & 71.76 \% & 59.29 \% & 0.1 s / 2 cores & 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.\\
MonoPair & & 61.57 \% & 78.81 \% & 56.51 \% & 0.06 s / GPU & 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.\\
monodle & & 61.29 \% & 78.66 \% & 56.18 \% & 0.04 s / GPU & 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 .\\
OGMMDet & & 61.26 \% & 72.41 \% & 58.79 \% & 0.01 s / 1 core & \\
ANM & & 61.26 \% & 72.41 \% & 58.79 \% & ANM / & \\
RPN+BF & & 61.22 \% & 77.06 \% & 55.22 \% & 0.6 s / GPU & L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.\\
focalnet & & 61.03 \% & 69.13 \% & 58.92 \% & 0.05 s / 1 core & \\
focalnet & & 60.99 \% & 69.06 \% & 58.89 \% & 0.05 s / 1 core & \\
3ONet & & 60.89 \% & 72.45 \% & 56.65 \% & 0.1 s / 1 core & H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.\\
Regionlets & & 60.83 \% & 73.79 \% & 54.72 \% & 1 s / >8 cores & X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.\\
3DSSD & & 60.51 \% & 72.33 \% & 56.28 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
BPG3D & & 60.24 \% & 69.19 \% & 56.74 \% & 0.05 s / 1 core & \\
ACFNet & & 60.12 \% & 71.42 \% & 55.96 \% & 0.11 s / 1 core & 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.\\
OFFNet & & 60.03 \% & 67.58 \% & 57.71 \% & 0.1 s / GPU & \\
KPTr & & 59.79 \% & 69.70 \% & 56.03 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LGSLNet & & 59.58 \% & 68.54 \% & 57.34 \% & 0.1 s / GPU & \\
IOUFusion & & 59.52 \% & 69.10 \% & 55.41 \% & 0.1 s / GPU & \\
DPPFA-Net & & 59.52 \% & 67.68 \% & 56.87 \% & 0.1 s / 1 core & J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.\\
ACDet & & 59.51 \% & 71.27 \% & 57.03 \% & 0.05 s / 1 core & J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.\\
SCNet3D & & 59.47 \% & 69.32 \% & 56.96 \% & 0.08 s / 1 core & \\
PV-RCNN-Plus & & 59.26 \% & 67.99 \% & 56.41 \% & 1 s / 1 core & \\
CZY\_PPF\_Net & & 59.26 \% & 67.81 \% & 57.04 \% & 0.1 s / 1 core & \\
QD-3DT & on & 59.26 \% & 78.41 \% & 54.37 \% & 0.03 s / GPU & H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.\\
LGNet-3classes & & 59.19 \% & 68.92 \% & 56.75 \% & 0.11 s / 1 core & \\
TANet & & 59.07 \% & 69.90 \% & 56.44 \% & 0.035s / GPU & 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.\\
MonoUNI & & 58.97 \% & 76.17 \% & 53.99 \% & 0.04 s / 1 core & 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.\\
DSA-PV-RCNN & la & 58.81 \% & 66.93 \% & 56.57 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
PSMS-Net & la & 58.81 \% & 70.59 \% & 56.27 \% & 0.1 s / 1 core & \\
SRDL & & 58.70 \% & 68.45 \% & 56.23 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
MMLab PV-RCNN & la & 58.37 \% & 68.88 \% & 55.38 \% & 0.08 s / 1 core & 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.\\
casxv1 & & 58.34 \% & 71.44 \% & 55.96 \% & 0.01 s / 1 core & \\
PASS-PV-RCNN-Plus & & 58.31 \% & 67.45 \% & 55.92 \% & 1 s / 1 core & Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.\\
focal & & 58.23 \% & 66.27 \% & 56.06 \% & 100 s / 1 core & \\
Point-GNN & la & 58.20 \% & 71.59 \% & 54.06 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
DeepParts & & 58.15 \% & 71.47 \% & 51.92 \% & ~1 s / GPU & Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.\\
CompACT-Deep & & 58.14 \% & 70.93 \% & 52.29 \% & 1 s / 1 core & Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.\\
EPNet++ & & 58.10 \% & 68.58 \% & 55.58 \% & 0.1 s / GPU & 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.\\
DSGN++ & st & 58.09 \% & 69.70 \% & 54.45 \% & 0.2 s / & 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.\\
MMLab-PartA^2 & la & 57.96 \% & 68.78 \% & 54.01 \% & 0.08 s / GPU & 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.\\
SVGA-Net & & 57.92 \% & 67.81 \% & 55.25 \% & 0.03s / 1 core & 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.\\
RAFDet & & 57.89 \% & 67.85 \% & 55.67 \% & 0.01 s / 1 core & \\
AVOD-FPN & la & 57.87 \% & 67.95 \% & 55.23 \% & 0.1 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
DFAF3D & & 57.65 \% & 67.45 \% & 53.89 \% & 0.05 s / 1 core & 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.\\
HA-PillarNet & & 57.59 \% & 66.29 \% & 55.14 \% & 0.05 s / 1 core & \\
PA-Det3D & & 57.55 \% & 66.80 \% & 55.18 \% & 0.06 s / 1 core & \\
U\_PV\_V2\_ep100\_80 & & 57.50 \% & 66.11 \% & 55.21 \% & 0... s / 1 core & \\
Faraway-Frustum & la & 57.35 \% & 67.88 \% & 54.42 \% & 0.1 s / GPU & 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.\\
PDV & & 57.34 \% & 65.94 \% & 54.21 \% & 0.1 s / 1 core & J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.\\
SIF & & 57.32 \% & 67.78 \% & 54.86 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
PG-RCNN & & 57.31 \% & 67.77 \% & 54.83 \% & 0.06 s / GPU & I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.\\
VPA & & 57.27 \% & 70.06 \% & 54.83 \% & 0.01 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
FromVoxelToPoint & & 57.26 \% & 68.26 \% & 54.74 \% & 0.1 s / 1 core & 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.\\
u\_second\_v4\_epoch\_10 & & 57.25 \% & 67.10 \% & 55.01 \% & 0.1 s / 1 core & \\
MLFusion-VS & & 57.22 \% & 66.13 \% & 55.00 \% & 0.06 s / 1 core & \\
SemanticVoxels & & 57.22 \% & 67.62 \% & 54.90 \% & 0.04 s / GPU & J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.\\
LVFSD & & 57.20 \% & 67.44 \% & 54.67 \% & 0.06 s / & ERROR: Wrong syntax in BIBTEX file.\\
centerpoint\_pcdet & & 57.06 \% & 65.95 \% & 55.08 \% & 0.06 s / 1 core & \\
IIOU & & 57.05 \% & 66.36 \% & 53.06 \% & 0.1 s / GPU & \\
Anonymous & & 56.90 \% & 70.04 \% & 52.69 \% & 0.04 s / 1 core & \\
DiffCandiDet & & 56.89 \% & 68.21 \% & 54.49 \% & 0.06 s / GPU & \\
IA-SSD (single) & & 56.87 \% & 66.69 \% & 54.68 \% & 0.013 s / 1 core & 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.\\
SFA-GCL(80, k=4) & & 56.83 \% & 69.60 \% & 54.42 \% & 0.04 s / 1 core & \\
CAT-Det & & 56.75 \% & 67.15 \% & 53.44 \% & 0.3 s / GPU & Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.\\
SFA-GCL\_dataaug & & 56.73 \% & 69.55 \% & 54.32 \% & 0.04 s / 1 core & \\
SFA-GCL(80) & & 56.69 \% & 67.64 \% & 52.51 \% & 0.04 s / 1 core & \\
FRCNN+Or & & 56.68 \% & 71.64 \% & 51.53 \% & 0.09 s / & C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.\\
SFA-GCL & & 56.62 \% & 69.30 \% & 54.20 \% & 0.04 s / 1 core & \\
FilteredICF & & 56.53 \% & 69.79 \% & 50.32 \% & ~ 2 s / >8 cores & S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.\\
SFA-GCL(baseline) & & 56.42 \% & 69.02 \% & 54.05 \% & 0.04 s / 1 core & \\
ARPNET & & 56.42 \% & 69.08 \% & 52.69 \% & 0.08 s / GPU & Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.\\
MonoRUn & & 56.40 \% & 73.05 \% & 51.40 \% & 0.07 s / GPU & 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.\\
RAFDet & & 56.25 \% & 66.03 \% & 52.79 \% & 0.01 s / 1 core & \\
MV-RGBD-RF & la & 56.18 \% & 72.99 \% & 49.72 \% & 4 s / 4 cores & A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.\\
U\_PV\_V2\_ep\_100\_100 & & 56.18 \% & 64.52 \% & 54.12 \% & 0.1 s / 1 core & \\
voxelnext\_pcdet & & 56.13 \% & 65.44 \% & 53.73 \% & 0.05 s / 1 core & \\
U\_second\_v4\_ep\_100\_8 & & 56.03 \% & 65.94 \% & 53.94 \% & 0.1 s / 1 core & \\
CG-SSD & & 55.97 \% & 65.50 \% & 53.64 \% & 0.01 s / 1 core & \\
HAF-PVP\_test & & 55.96 \% & 65.29 \% & 53.25 \% & 0.09 s / 1 core & \\
HMFI & & 55.96 \% & 66.20 \% & 53.24 \% & 0.1 s / 1 core & 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.\\
MGAF-3DSSD & & 55.80 \% & 66.31 \% & 52.02 \% & 0.1 s / 1 core & 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.\\
MG & & 55.70 \% & 64.24 \% & 52.17 \% & 0.1 s / 1 core & \\
GUPNet & & 55.65 \% & 74.95 \% & 48.44 \% & NA s / 1 core & 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.\\
MLOD & la & 55.62 \% & 68.42 \% & 51.45 \% & 0.12 s / GPU & J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.\\
RAFDet & & 55.41 \% & 64.91 \% & 53.19 \% & 0.1 s / 1 core & \\
DEVIANT & & 55.16 \% & 74.27 \% & 50.21 \% & 0.04 s / & 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.\\
PointPillars & la & 55.10 \% & 65.29 \% & 52.39 \% & 16 ms / & A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.\\
StereoDistill & & 55.09 \% & 69.00 \% & 50.95 \% & 0.4 s / 1 core & 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.\\
STD & & 55.04 \% & 68.33 \% & 50.85 \% & 0.08 s / GPU & Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.\\
GeVo & & 55.04 \% & 63.95 \% & 52.93 \% & 0.05 s / 1 core & \\
DGEnhCL & & 55.01 \% & 67.98 \% & 52.59 \% & 0.04 s / 1 core & \\
OPA-3D & & 54.92 \% & 73.93 \% & 47.87 \% & 0.04 s / 1 core & 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.\\
SC-SSD & & 54.83 \% & 64.15 \% & 52.65 \% & 1 s / 1 core & \\
Vote3Deep & la & 54.80 \% & 67.99 \% & 51.17 \% & 1.5 s / 4 cores & M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.\\
M3DeTR & & 54.78 \% & 63.15 \% & 52.49 \% & n/a s / GPU & 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.\\
MFB3D & & 54.75 \% & 62.76 \% & 51.89 \% & 0.14 s / 1 core & \\
L-AUG & & 54.61 \% & 65.71 \% & 51.67 \% & 0.1 s / 1 core & T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.\\
TF-PartA2 & & 54.37 \% & 64.13 \% & 50.68 \% & 0.1 s / 1 core & \\
BAPartA2S-4h & & 54.34 \% & 64.22 \% & 51.53 \% & 0.1 s / 1 core & \\
SFA-GCL & & 54.27 \% & 66.81 \% & 50.13 \% & 0.04 s / 1 core & \\
epBRM & la & 54.13 \% & 62.90 \% & 51.95 \% & 0.10 s / 1 core & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
DFSemONet(Baseline) & & 54.13 \% & 65.42 \% & 52.05 \% & 0.04 s / GPU & \\
DVFENet & & 54.13 \% & 63.54 \% & 51.79 \% & 0.05 s / 1 core & 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.\\
XView & & 53.83 \% & 62.27 \% & 51.61 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
PointPainting & la & 53.76 \% & 61.86 \% & 50.61 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
PDV2 & & 53.54 \% & 65.59 \% & 47.65 \% & 3.7 s / 1 core & J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.\\
PR-SSD & & 53.52 \% & 62.55 \% & 50.04 \% & 0.02 s / GPU & \\
Mix-Teaching & & 53.52 \% & 67.34 \% & 47.45 \% & 30 s / 1 core & 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.\\
AMVFNet & & 53.28 \% & 62.79 \% & 49.69 \% & 0.04 s / GPU & \\
Cube R-CNN & & 53.27 \% & 64.96 \% & 47.84 \% & 0.05 s / GPU & 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.\\
GF-pointnet & & 53.26 \% & 62.91 \% & 50.71 \% & 0.02 s / 1 core & \\
TAFT & & 53.15 \% & 67.62 \% & 47.08 \% & 0.2 s / 1 core & J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.\\
Disp R-CNN & st & 52.98 \% & 71.79 \% & 48.20 \% & 0.387 s / GPU & 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.\\
PI-SECOND & & 52.97 \% & 63.28 \% & 49.07 \% & 0.05 s / GPU & \\
Disp R-CNN (velo) & st & 52.90 \% & 71.63 \% & 48.15 \% & 0.387 s / GPU & 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.\\
pAUCEnsT & & 52.88 \% & 65.84 \% & 46.97 \% & 60 s / 1 core & S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.\\
SparVox3D & & 52.84 \% & 69.33 \% & 48.49 \% & 0.05 s / GPU & 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.\\
AAMVFNet & & 52.73 \% & 63.13 \% & 50.44 \% & 0.04 s / GPU & \\
MonoAIU & & 52.68 \% & 71.73 \% & 45.61 \% & 0.03 s / GPU & \\
GSG-FPS & & 52.66 \% & 61.73 \% & 50.50 \% & 0.01 s / 1 core & \\
PFF3D & la & 52.53 \% & 62.12 \% & 50.27 \% & 0.05 s / GPU & 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.\\
IA-SSD (multi) & & 52.45 \% & 65.07 \% & 50.20 \% & 0.014 s / 1 core & 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.\\
S-AT GCN & & 52.30 \% & 62.01 \% & 50.10 \% & 0.02 s / GPU & 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.\\
MMLAB LIGA-Stereo & st & 52.18 \% & 65.59 \% & 49.29 \% & 0.4 s / 1 core & 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.\\
HINTED & & 51.95 \% & 66.52 \% & 47.83 \% & 0.04 s / 1 core & \\
XT-PartA2 & & 51.93 \% & 61.04 \% & 49.32 \% & 0.1 s / GPU & \\
bs & & 51.66 \% & 60.30 \% & 49.41 \% & 0.1 s / 1 core & \\
Plane-Constraints & & 51.57 \% & 64.64 \% & 46.98 \% & 0.05 s / 4 cores & 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.\\
PUDet & & 51.41 \% & 62.39 \% & 49.08 \% & 0.3 s / GPU & \\
mm3d\_PartA2 & & 51.40 \% & 60.60 \% & 48.39 \% & 0.1 s / GPU & \\
Test\_dif & & 51.35 \% & 60.86 \% & 49.27 \% & 0.01 s / 1 core & \\
Shift R-CNN (mono) & & 51.30 \% & 70.86 \% & 46.37 \% & 0.25 s / GPU & 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.\\
SeSame-point & & 51.27 \% & 60.29 \% & 49.06 \% & N/A s / TITAN RTX & \\
VoxelFSD-S & & 51.00 \% & 60.60 \% & 48.71 \% & 0.05 s / 1 core & \\
SeSame-voxel & & 49.74 \% & 60.69 \% & 45.64 \% & N/A s / TITAN RTX & \\
SCNet & la & 49.61 \% & 60.95 \% & 46.91 \% & 0.04 s / GPU & 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.\\
MM\_SECOND & & 49.60 \% & 60.59 \% & 46.70 \% & 0.05 s / GPU & \\
MMLab-PointRCNN & la & 49.41 \% & 58.93 \% & 46.44 \% & 0.1 s / GPU & 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.\\
MonoSIM\_v2 & & 49.01 \% & 63.65 \% & 42.86 \% & 0.03 s / 1 core & \\
HomoLoss(monoflex) & & 48.97 \% & 63.77 \% & 44.60 \% & 0.04 s / 1 core & 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.\\
mdab & & 48.97 \% & 65.87 \% & 44.11 \% & 22 s / 1 core & \\
MMpointpillars & & 48.95 \% & 60.01 \% & 46.12 \% & 0.05 s / 1 core & \\
HA PillarNet & & 48.78 \% & 59.59 \% & 46.03 \% & 0.05 s / 1 core & \\
mdab & & 48.66 \% & 65.70 \% & 43.93 \% & 22 s / 1 core & \\
ACFD & la & 48.63 \% & 61.62 \% & 44.15 \% & 0.2 s / 4 cores & M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.\\
R-CNN & & 48.57 \% & 62.88 \% & 43.05 \% & 4 s / GPU & J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.\\
prcnn\_v18\_80\_100 & & 48.53 \% & 60.07 \% & 45.72 \% & 0.1 s / 1 core & \\
Anonymous & & 48.51 \% & 65.02 \% & 41.77 \% & 0.1 s / 1 core & \\
GraphAlign(ICCV2023) & & 48.47 \% & 55.17 \% & 46.68 \% & 0.03 s / GPU & 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.\\
IIOU\_LDR & & 48.40 \% & 60.07 \% & 46.41 \% & 0.03 s / 1 core & \\
VSAC & & 48.22 \% & 60.72 \% & 45.55 \% & 0.07 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
ROT\_S3D & & 48.11 \% & 59.38 \% & 46.18 \% & 0.1 s / GPU & \\
MonoLiG & & 47.69 \% & 62.87 \% & 43.27 \% & 0.03 s / 1 core & A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.\\
MonoFlex & & 47.58 \% & 62.64 \% & 43.15 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
BirdNet+ & la & 47.50 \% & 54.78 \% & 45.53 \% & 0.11 s / & 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.\\
CMKD & & 46.84 \% & 61.04 \% & 42.92 \% & 0.1 s / 1 core & Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.\\
MonOAPC & & 46.31 \% & 60.93 \% & 42.05 \% & 0035 s / 1 core & 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.\\
MMpp & & 46.09 \% & 56.10 \% & 43.62 \% & 0.05 s / 1 core & \\
SS3D & & 45.79 \% & 61.58 \% & 41.14 \% & 48 ms / & 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.\\
MonoRCNN++ & & 45.76 \% & 60.29 \% & 39.39 \% & 0.07 s / GPU & X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.\\
ACF & & 45.67 \% & 59.81 \% & 40.88 \% & 1 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.\\
SH3D & & 45.64 \% & 59.74 \% & 41.29 \% & 0.1 s / 1 core & \\
Fusion-DPM & la & 44.99 \% & 58.93 \% & 40.19 \% & ~ 30 s / 1 core & C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.\\
ACF-MR & & 44.79 \% & 58.29 \% & 39.94 \% & 0.6 s / 1 core & R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.\\
MonoTRKDv2 & & 44.54 \% & 59.66 \% & 40.12 \% & 40 s / 1 core & \\
P2P & & 44.30 \% & 55.25 \% & 42.40 \% & 0.1 s / GPU & \\
SeSame-pillar & & 44.21 \% & 52.67 \% & 41.95 \% & N/A s / TITAN RTX & \\
LPCG-Monoflex & & 44.13 \% & 62.44 \% & 39.46 \% & 0.03 s / 1 core & 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.\\
HA-SSVM & & 43.87 \% & 58.76 \% & 38.81 \% & 21 s / 1 core & J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.\\
AB3DMOT & la on & 43.86 \% & 54.55 \% & 40.99 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
MonoEF & & 43.73 \% & 58.79 \% & 39.45 \% & 0.03 s / 1 core & 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.\\
D4LCN & & 43.50 \% & 59.55 \% & 37.12 \% & 0.2 s / GPU & 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.\\
DMF & st & 43.43 \% & 52.99 \% & 41.29 \% & 0.2 s / 1 core & X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.\\
MonoDDE & & 43.36 \% & 57.80 \% & 39.00 \% & 0.04 s / 1 core & 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.\\
DPM-VOC+VP & & 43.26 \% & 59.21 \% & 38.12 \% & 8 s / 1 core & B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.\\
ACF-SC & & 42.97 \% & 53.30 \% & 38.12 \% & & C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.\\
SeSame-voxel w/score & & 42.88 \% & 50.84 \% & 40.76 \% & N/A s / GPU & \\
MonoDTR & & 42.86 \% & 59.44 \% & 38.57 \% & 0.04 s / 1 core & K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.\\
SquaresICF & & 42.61 \% & 57.08 \% & 37.85 \% & 1 s / GPU & R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.\\
CG-Stereo & st & 42.54 \% & 54.64 \% & 38.45 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
MonoAuxNorm & & 42.32 \% & 56.80 \% & 37.86 \% & 0.02 s / GPU & \\
BirdNet+ (legacy) & la & 41.97 \% & 51.38 \% & 40.15 \% & 0.1 s / & 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.\\
DDMP-3D & & 41.54 \% & 56.73 \% & 35.52 \% & 0.18 s / 1 core & 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.\\
CSW3D & la & 41.50 \% & 53.76 \% & 37.25 \% & 0.03 s / 4 cores & J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.\\
M3D-RPN & & 41.46 \% & 56.64 \% & 37.31 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
YOLOStereo3D & st & 41.46 \% & 56.20 \% & 37.07 \% & 0.1 s / & 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.\\
MonoFRD & & 41.20 \% & 54.06 \% & 37.53 \% & 0.01 s / 1 core & \\
BKDStereo3D & & 41.17 \% & 55.94 \% & 34.99 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
CIE & & 41.04 \% & 53.27 \% & 37.73 \% & 0.1 s / 1 core & Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.\\
SubCat & & 40.50 \% & 53.75 \% & 35.66 \% & 1.2 s / 6 cores & E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.\\
PS-fld & & 40.47 \% & 55.47 \% & 36.65 \% & 0.25 s / 1 core & 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.\\
SeSame-pillar w/scor & & 40.24 \% & 48.38 \% & 38.25 \% & N/A s / 1 core & \\
DSGN & st & 39.93 \% & 49.28 \% & 38.13 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
RT3D-GMP & st & 39.83 \% & 55.56 \% & 35.18 \% & 0.06 s / GPU & 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.\\
SparsePool & & 39.59 \% & 50.81 \% & 35.91 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
SparsePool & & 39.43 \% & 50.94 \% & 35.78 \% & 0.13 s / 8 cores & Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.\\
AVOD & la & 39.43 \% & 50.90 \% & 35.75 \% & 0.08 s / & J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.\\
ACF & & 39.12 \% & 48.42 \% & 35.03 \% & 0.2 s / 1 core & P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .\\
MonoTAKD V2 & & 38.60 \% & 54.33 \% & 34.12 \% & 0.1 s / 1 core & \\
MonoLTKD & & 38.60 \% & 54.33 \% & 34.12 \% & 0.04 s / 1 core & \\
MonoTAKD & & 38.60 \% & 54.33 \% & 34.12 \% & 0.1 s / 1 core & \\
MonoLTKD\_V3 & & 38.60 \% & 54.33 \% & 34.12 \% & 0.04 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LSVM-MDPM-sv & & 37.26 \% & 50.74 \% & 33.13 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.\\
ODGS & & 37.12 \% & 46.01 \% & 34.56 \% & 0.1 s / 1 core & \\
BKDStereo3D w/o KD & & 37.02 \% & 50.58 \% & 32.92 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
multi-task CNN & & 37.00 \% & 49.38 \% & 33.46 \% & 25.1 ms / GPU & M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.\\
MonoSIM & & 36.71 \% & 49.49 \% & 33.24 \% & 0.16 s / 1 core & \\
SFEBEV & & 36.60 \% & 45.47 \% & 34.70 \% & 0.01 s / 1 core & \\
Complexer-YOLO & la & 36.45 \% & 42.16 \% & 32.91 \% & 0.06 s / GPU & 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.\\
LSVM-MDPM-us & & 35.92 \% & 48.73 \% & 31.70 \% & 10 s / 4 cores & P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.\\
error & & 35.90 \% & 48.27 \% & 32.44 \% & 1 s / 1 core & \\
CMAN & & 34.96 \% & 49.73 \% & 30.92 \% & 0.15 s / 1 core & C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.\\
Aug3D-RPN & & 34.95 \% & 47.22 \% & 30.64 \% & 0.08 s / 1 core & C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.\\
FMF-occlusion-net & & 34.74 \% & 49.26 \% & 30.37 \% & 0.16 s / 1 core & 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.\\
MonoNeRD & & 34.43 \% & 46.50 \% & 31.06 \% & na s / 1 core & 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.\\
PS-SVDM & & 34.15 \% & 46.43 \% & 30.90 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
mdab & & 34.04 \% & 47.88 \% & 31.26 \% & 0.02 s / 1 core & \\
PointRGBNet & & 33.92 \% & 44.35 \% & 30.43 \% & 0.08 s / 4 cores & P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.\\
PGD-FCOS3D & & 33.67 \% & 48.30 \% & 29.76 \% & 0.03 s / 1 core & T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.\\
Vote3D & la & 33.04 \% & 42.66 \% & 30.59 \% & 0.5 s / 4 cores & D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.\\
ESGN & st & 32.60 \% & 44.09 \% & 29.10 \% & 0.06 s / GPU & 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.\\
SGM3D & & 32.48 \% & 45.03 \% & 28.58 \% & 0.03 s / 1 core & 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.\\
CaDDN & & 32.42 \% & 46.35 \% & 29.98 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
PS-SVDM & & 32.24 \% & 44.02 \% & 29.08 \% & 1 s / 1 core & Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.\\
DFR-Net & & 31.84 \% & 45.20 \% & 27.94 \% & 0.18 s / & 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.\\
OC Stereo & st & 30.79 \% & 43.50 \% & 28.40 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
mBoW & la & 30.26 \% & 41.52 \% & 26.34 \% & 10 s / 1 core & 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.\\
BirdNet & la & 30.07 \% & 36.82 \% & 28.40 \% & 0.11 s / & 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.\\
SeSame-point w/score & & 30.04 \% & 40.65 \% & 27.65 \% & N/A s / GPU & \\
RT3DStereo & st & 29.30 \% & 41.12 \% & 25.25 \% & 0.08 s / GPU & 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.\\
MDSNet & & 29.25 \% & 41.64 \% & 26.01 \% & 0.05 s / 1 core & 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.\\
SST [st] & st & 26.78 \% & 38.41 \% & 24.58 \% & 1 s / 1 core & \\
DPM-C8B1 & st & 25.34 \% & 36.40 \% & 22.00 \% & 15 s / 4 cores & J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.\\
RefinedMPL & & 20.81 \% & 30.41 \% & 18.72 \% & 0.15 s / GPU & J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.\\
TopNet-Retina & la & 16.45 \% & 22.37 \% & 15.43 \% & 52ms / & 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.\\
TopNet-HighRes & la & 15.28 \% & 21.22 \% & 13.89 \% & 101ms / & 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.\\
YOLOv2 & & 11.46 \% & 15.37 \% & 9.67 \% & 0.02 s / GPU & J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.\\
MonoGhost\_Ped\_Cycl & & 9.80 \% & 13.31 \% & 9.91 \% & 0.03 s / 1 core & \\
TopNet-UncEst & la & 8.58 \% & 13.00 \% & 7.38 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
BIP-HETERO & & 7.05 \% & 8.51 \% & 6.30 \% & ~2 s / 1 core & A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.\\
init & & 0.03 \% & 0.03 \% & 0.03 \% & 0.03 s / 1 core & \\
mdab & & 0.02 \% & 0.02 \% & 0.02 \% & 0.02 s / 1 core & \\
TopNet-DecayRate & la & 0.01 \% & 0.01 \% & 0.01 \% & 92 ms / & 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.\\
f3sd & & 0.01 \% & 0.01 \% & 0.01 \% & 1.67 s / 1 core & \\
DA3D+KM3D+v2-99 & & 0.00 \% & 0.00 \% & 0.00 \% & 0.120s / GPU & 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.\\
DA3D+KM3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.02 s / GPU & 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.\\
DA3D & & 0.00 \% & 0.00 \% & 0.00 \% & 0.03 s / 1 core & 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.
\end{tabular}