\begin{tabular}{c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Moderate} & {\bf Easy} & {\bf Hard} & {\bf Runtime} & {\bf Environment}\\ \hline
SFD & & 84.76 \% & 91.73 \% & 77.92 \% & 0.1 s / 1 core & \\
Anonymous & & 83.96 \% & 90.83 \% & 77.47 \% & n/a s / 1 core & \\
DGDNH & & 83.88 \% & 90.69 \% & 79.50 \% & 0.04 s / 1 core & \\
VPFNet & & 83.21 \% & 91.02 \% & 78.20 \% & 0.06 s / 2 cores & \\
Anonymous & & 82.99 \% & 91.64 \% & 78.02 \% & 0.1 s / GPU & \\
NFAF3D & & 82.97 \% & 91.57 \% & 77.72 \% & 0.06 s / 1 core & \\
BtcDet & la & 82.86 \% & 90.64 \% & 78.09 \% & 0.09 s / GPU & \\
Anonymous & & 82.79 \% & 91.30 \% & 78.07 \% & n/a s / 1 core & \\
PE-RCVN & & 82.69 \% & 91.51 \% & 77.75 \% & 0.03 s / 1 core & \\
SPG\_mini & la & 82.66 \% & 90.64 \% & 77.91 \% & 0.09 s / GPU & \\
DSASNet & & 82.63 \% & 89.48 \% & 77.94 \% & 0.08 s / GPU & \\
SE-SSD & la & 82.54 \% & 91.49 \% & 77.15 \% & 0.03 s / 1 core & W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.\\
TF3D & la & 82.46 \% & 89.10 \% & 77.78 \% & 0.1 s / 2 cores & \\
DVF-V & & 82.45 \% & 89.40 \% & 77.56 \% & 0.1 s / 1 core & \\
DVF-PV & & 82.40 \% & 90.99 \% & 77.37 \% & 0.1 s / 1 core & \\
EA-M-RCNN(BorderAtt) & & 82.33 \% & 87.77 \% & 77.37 \% & 0.08 s / 1 core & \\
NFAF3D-light & & 82.30 \% & 90.88 \% & 76.89 \% & 0.03 s / 1 core & \\
Anonymous & & 82.28 \% & 90.55 \% & 77.59 \% & 0.1 s / 1 core & \\
CLOCs & & 82.28 \% & 89.16 \% & 77.23 \% & 0.1 s / 1 core & 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.\\
TBD & la & 82.23 \% & 88.76 \% & 77.48 \% & 0.1 s / 1 core & \\
CityBrainLab & & 82.19 \% & 90.51 \% & 77.17 \% & 0.04 s / 1 core & \\
SPG & la & 82.13 \% & 90.50 \% & 78.90 \% & 0.09 s / 1 core & 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.\\
VoTr-TSD & & 82.09 \% & 89.90 \% & 79.14 \% & 0.07 s / 1 core & 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.\\
TBD & & 82.09 \% & 89.50 \% & 79.29 \% & 0.1 s / 1 core & \\
Pyramid R-CNN & & 82.08 \% & 88.39 \% & 77.49 \% & 0.07 s / 1 core & 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.\\
FS-Net & la & 82.07 \% & 88.68 \% & 77.42 \% & 0.1 s / 1 core & \\
VoxSeT & & 82.06 \% & 88.53 \% & 77.46 \% & 0.09 s / 1 core & \\
SRIF-RCNN & & 82.04 \% & 88.45 \% & 77.54 \% & 0.0947 s / 1 core & \\
EQ-PVRCNN & & 82.01 \% & 90.13 \% & 77.53 \% & 0.2 s / GPU & \\
anonymous & & 81.99 \% & 88.82 \% & 77.26 \% & 0.09 s / GPU & \\
Anonymous & & 81.96 \% & 89.90 \% & 77.20 \% & 0.1s / 1 core & \\
EPNet++ & & 81.96 \% & 91.37 \% & 76.71 \% & 0.1 s / GPU & \\
PV-RCNN++ & & 81.88 \% & 90.14 \% & 77.15 \% & 0.06 s / 1 core & \\
PDV & & 81.86 \% & 90.43 \% & 77.36 \% & 0.1 s / 1 core & \\
SGNet & & 81.85 \% & 88.83 \% & 77.47 \% & 0.09 s / GPU & \\
Anonymous & & 81.85 \% & 89.96 \% & 76.51 \% & 0.06 s / 1 core & \\
ST-RCNN & la & 81.84 \% & 90.50 \% & 77.22 \% & 0.04 s / 1 core & \\
ST-RCNN (SNLW-RCNN) & la & 81.84 \% & 90.50 \% & 77.22 \% & 0.04 s / 1 core & \\
ISE-RCNN & & 81.83 \% & 89.12 \% & 77.29 \% & 0.09 s / 1 core & \\
SqueezeRCNN & & 81.80 \% & 88.72 \% & 77.10 \% & 0.08 s / 1 core & \\
CityBrainLab-CT3D & & 81.77 \% & 87.83 \% & 77.16 \% & 0.07 s / 1 core & 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.\\
JPVNet & & 81.73 \% & 88.66 \% & 76.94 \% & 0.08 s / 1 core & \\
TBD & & 81.73 \% & 89.48 \% & 79.05 \% & 0.1 s / 1 core & \\
M3DeTR & & 81.73 \% & 90.28 \% & 76.96 \% & 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.\\
SIENet & & 81.71 \% & 88.22 \% & 77.22 \% & 0.08 s / 1 core & Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.\\
E^2-PV-RCNN & & 81.70 \% & 88.33 \% & 77.20 \% & 0.08 s / 1 core & \\
PLNL-3DSSD & la & 81.69 \% & 88.98 \% & 74.90 \% & 0.08 s / GPU & \\
VCRCNN & & 81.68 \% & 90.52 \% & 77.26 \% & 0.1 s / 1 core & \\
TBD & & 81.68 \% & 87.93 \% & 76.92 \% & 0.1 s / 1 core & \\
ASCNet & & 81.67 \% & 88.48 \% & 76.93 \% & 0.09 s / 1 core & \\
Fast VP-RCNN & & 81.62 \% & 90.97 \% & 76.90 \% & 0.05 s / 1 core & \\
Voxel R-CNN & & 81.62 \% & 90.90 \% & 77.06 \% & 0.04 s / GPU & 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.\\
BANet & & 81.61 \% & 89.28 \% & 76.58 \% & 0.11 s / 1 core & R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.\\
SARFE & & 81.59 \% & 88.88 \% & 76.74 \% & 0.03 s / 1 core & \\
HyBrid Feature Det & & 81.59 \% & 88.77 \% & 76.92 \% & 0.08 s / 1 core & \\
FromVoxelToPoint & & 81.58 \% & 88.53 \% & 77.37 \% & 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.\\
H^23D R-CNN & & 81.55 \% & 90.43 \% & 77.22 \% & 0.03 s / 1 core & 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.\\
anonymous & & 81.55 \% & 90.94 \% & 76.74 \% & 0.05s / 1 core & \\
LZY\_RCNN & & 81.52 \% & 88.77 \% & 78.59 \% & 0.08 s / 1 core & \\
TBD & & 81.51 \% & 88.96 \% & 77.27 \% & 0.1 s / 1 core & \\
MSG-PGNN & & 81.50 \% & 88.70 \% & 76.88 \% & 0.08 s / 1 core & \\
TransCyclistNet & & 81.46 \% & 88.47 \% & 76.87 \% & 0.08 s / 1 core & \\
DSA-PV-RCNN & la & 81.46 \% & 88.25 \% & 76.96 \% & 0.08 s / 1 core & P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.\\
P2V-RCNN & & 81.45 \% & 88.34 \% & 77.20 \% & 0.1 s / 1 core & 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.\\
MMLab PV-RCNN & la & 81.43 \% & 90.25 \% & 76.82 \% & 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.\\
TPCG & & 81.41 \% & 89.16 \% & 76.90 \% & 0.1 s / 1 core & \\
PC-RGNN & & 81.38 \% & 87.94 \% & 76.88 \% & 0.1 s / GPU & \\
DDet & & 81.38 \% & 89.63 \% & 78.57 \% & 0.1 s / 1 core & \\
WHUT-iou\_ssd & & 81.37 \% & 89.84 \% & 76.83 \% & 0.045s / 1 core & \\
XView & & 81.35 \% & 89.21 \% & 76.87 \% & 0.1 s / 1 core & L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.\\
ISE-RCNN-PV & & 81.34 \% & 88.05 \% & 76.99 \% & 0.1 s / 1 core & \\
RangeRCNN & la & 81.33 \% & 88.47 \% & 77.09 \% & 0.06 s / GPU & 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.\\
CAT-Det & & 81.32 \% & 89.87 \% & 76.68 \% & 0.3 s / GPU & \\
TransDet3D & & 81.28 \% & 88.11 \% & 76.73 \% & 0.08 s / 1 core & \\
Generalized-SIENet & & 81.24 \% & 87.70 \% & 76.79 \% & 0.08 s / 1 core & \\
Point Image Fusion & & 81.23 \% & 89.01 \% & 76.77 \% & 0.2 s / 1 core & \\
SAA-PV-RCNN & & 81.09 \% & 87.24 \% & 78.05 \% & 0.08 s / 1 core & \\
FPC-RCNN & & 81.08 \% & 88.68 \% & 76.46 \% & 0.05 s / 1 core & \\
VPFNet & & 80.97 \% & 88.51 \% & 76.74 \% & 0.2 s / 1 core & C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.\\
CSVoxel-RCNN & & 80.97 \% & 87.66 \% & 76.29 \% & 0.03 s / GPU & \\
VueronNet & & 80.96 \% & 90.06 \% & 73.72 \% & 0.06 s / 1 core & \\
FusionDetv2-v4 & & 80.93 \% & 87.75 \% & 76.12 \% & 0.06 s / 1 core & \\
AIMC-RUC & & 80.83 \% & 90.14 \% & 73.59 \% & 0.11 s / 1 core & \\
GNN-RCNN & & 80.81 \% & 87.94 \% & 76.53 \% & 0.1 s / 1 core & \\
sa-voxel-centernet & & 80.77 \% & 87.39 \% & 76.45 \% & 0.04 s / 1 core & \\
SA-voxel-centernet & & 80.77 \% & 87.28 \% & 76.51 \% & 0.04 s / 1 core & \\
Associate-3Ddet\_v2 & & 80.77 \% & 91.53 \% & 75.23 \% & 0.04 s / 1 core & \\
FusionDetv2-v3 & & 80.70 \% & 88.05 \% & 76.10 \% & 0.05 s / 1 core & \\
StructuralIF & & 80.69 \% & 87.15 \% & 76.26 \% & 0.02 s / 8 cores & J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.\\
CLOCs\_PVCas & & 80.67 \% & 88.94 \% & 77.15 \% & 0.1 s / 1 core & 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.\\
SCIR-Net & la & 80.62 \% & 87.53 \% & 76.00 \% & 0.03 s / GPU & \\
Sem-Aug v1 & & 80.40 \% & 88.92 \% & 77.37 \% & 0.04 s / GPU & \\
Fast-CLOCs & & 80.35 \% & 89.10 \% & 76.99 \% & 0.1 s / GPU & \\
FPV-SSD & & 80.34 \% & 87.72 \% & 75.40 \% & 0.03 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SPANet & & 80.34 \% & 91.05 \% & 74.89 \% & 0.06 s / 1 core & Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.\\
IA-SSD (single) & & 80.32 \% & 88.87 \% & 75.10 \% & 0.013 s / 1 core & \\
AM-SSD & & 80.30 \% & 89.58 \% & 75.02 \% & 0.04 s / 1 core & \\
CIA-SSD & la & 80.28 \% & 89.59 \% & 72.87 \% & 0.03 s / 1 core & 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.\\
FusionDetv1 & & 80.28 \% & 87.45 \% & 76.21 \% & 0.1 s / 1 core & \\
DVF & & 80.21 \% & 88.97 \% & 75.22 \% & 0.1 s / 1 core & \\
VCT & & 80.19 \% & 89.12 \% & 77.19 \% & 0.2 s / 1 core & \\
TBD & & 80.17 \% & 86.83 \% & 75.96 \% & 0.06 s / 1 core & \\
IA-SSD (multi) & & 80.13 \% & 88.34 \% & 75.04 \% & 0.014 s / 1 core & \\
TBD & & 80.12 \% & 88.30 \% & 75.29 \% & 0.01 s / 1 core & \\
EBM3DOD & & 80.12 \% & 91.05 \% & 72.78 \% & 0.12 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
3D-CVF at SPA & la & 80.05 \% & 89.20 \% & 73.11 \% & 0.06 s / 1 core & 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.\\
TBD & & 80.02 \% & 88.45 \% & 74.85 \% & TBD / GPU & \\
MVOD & & 80.01 \% & 88.53 \% & 77.24 \% & 0.16 s / 1 core & \\
MBDF-Net & & 80.00 \% & 90.87 \% & 75.04 \% & 0.1 s / 1 core & \\
SIF & & 79.88 \% & 86.84 \% & 75.89 \% & 0.1 s / 1 core & P. An: SIF. Submitted to CVIU 2021.\\
RangeIoUDet & la & 79.80 \% & 88.60 \% & 76.76 \% & 0.02 s / GPU & 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.\\
SA-SSD & & 79.79 \% & 88.75 \% & 74.16 \% & 0.04 s / 1 core & C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.\\
KpNet & & 79.75 \% & 88.92 \% & 72.17 \% & 0.42 s / 1 core & \\
KpNet & & 79.74 \% & 88.88 \% & 72.13 \% & 42 s / 1 core & \\
STD & & 79.71 \% & 87.95 \% & 75.09 \% & 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.\\
MGAF-3DSSD & & 79.68 \% & 88.16 \% & 72.39 \% & 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.\\
MBDF-Net-1 & & 79.65 \% & 90.43 \% & 74.72 \% & 0.1 s / 1 core & \\
Struc info fusion II & & 79.59 \% & 88.97 \% & 72.51 \% & 0.05 s / GPU & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
3DSSD & & 79.57 \% & 88.36 \% & 74.55 \% & 0.04 s / GPU & Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.\\
EBM3DOD baseline & & 79.52 \% & 88.80 \% & 72.30 \% & 0.05 s / 1 core & F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.\\
Struc info fusion I & & 79.49 \% & 88.70 \% & 74.25 \% & 0.05 s / 1 core & P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.\\
Point-GNN & la & 79.47 \% & 88.33 \% & 72.29 \% & 0.6 s / GPU & W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.\\
SECOND & & 79.46 \% & 87.44 \% & 73.97 \% & 0.04 s / 1 core & \\
RoIFusion & & 79.36 \% & 88.09 \% & 72.51 \% & 0.22 s / 1 core & \\
NV-RCNN & & 79.32 \% & 87.58 \% & 74.74 \% & 0.1 s / 1 core & \\
3DIoU\_v2 & & 79.30 \% & 88.22 \% & 76.96 \% & 0.2 s / 1 core & \\
EPNet & & 79.28 \% & 89.81 \% & 74.59 \% & 0.1 s / 1 core & T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.\\
FPCR-CNN & & 79.25 \% & 88.45 \% & 75.69 \% & 0.05 s / 1 core & \\
3DIoU++ & & 79.22 \% & 87.49 \% & 76.68 \% & 0.1 s / 1 core & \\
DVFENet & & 79.18 \% & 86.20 \% & 74.58 \% & 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.\\
Faraway-Frustum & la & 79.05 \% & 87.45 \% & 76.14 \% & 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. arXiv preprint arXiv:2011.01404 2020.\\
3D IoU-Net & & 79.03 \% & 87.96 \% & 72.78 \% & 0.1 s / 1 core & 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.\\
SERCNN & la & 78.96 \% & 87.74 \% & 74.30 \% & 0.1 s / 1 core & 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.\\
NV2P-RCNN & & 78.92 \% & 87.36 \% & 74.16 \% & 0.1 s / GPU & \\
demo & & 78.85 \% & 87.50 \% & 72.05 \% & 0.04 s / 1 core & \\
MSADet & & 78.81 \% & 88.31 \% & 73.82 \% & 0.05 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
FPC3D & ms & 78.81 \% & 87.61 \% & 75.49 \% & 33 s / 1 core & \\
MVAF-Net & & 78.71 \% & 87.87 \% & 75.48 \% & 0.06 s / 1 core & 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.\\
MMLab-PartA^2 & la & 78.49 \% & 87.81 \% & 73.51 \% & 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.\\
CLOCs\_SecCas & & 78.45 \% & 86.38 \% & 72.45 \% & 0.1 s / 1 core & 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.\\
FusionDetv2-v2 & & 78.42 \% & 86.59 \% & 73.87 \% & 0.04 s / 1 core & \\
Patches - EMP & la & 78.41 \% & 89.84 \% & 73.15 \% & 0.5 s / GPU & 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.\\
MKFFNet & & 78.40 \% & 85.25 \% & 73.75 \% & 0.1 s / 1 core & \\
HotSpotNet & & 78.31 \% & 87.60 \% & 73.34 \% & 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.\\
FusionDetv2-v5 & & 78.30 \% & 86.94 \% & 73.44 \% & 0.05 s / 1 core & \\
MKFFNet & & 78.30 \% & 87.25 \% & 73.66 \% & 0.1 s / 1 core & \\
MKFFNet & & 78.30 \% & 86.86 \% & 73.80 \% & 0.01s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
SAA-SECOND & & 78.13 \% & 86.13 \% & 73.34 \% & 38m s / 1 core & \\
3D-VDNet & & 78.05 \% & 87.13 \% & 72.90 \% & 0.03 s / 1 core & \\
VPN & & 77.93 \% & 85.02 \% & 72.97 \% & 0.06 s / 1 core & \\
CenterNet3D & & 77.90 \% & 86.20 \% & 73.03 \% & 0.04 s / GPU & G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.\\
CVFNet & & 77.70 \% & 88.75 \% & 71.95 \% & 28.1ms / 1 core & \\
VGCN & & 77.65 \% & 84.47 \% & 73.36 \% & 0.09 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
AutoAlign & & 77.58 \% & 86.84 \% & 73.23 \% & 0.1 s / 1 core & \\
UberATG-MMF & la & 77.43 \% & 88.40 \% & 70.22 \% & 0.08 s / GPU & M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.\\
Associate-3Ddet & & 77.40 \% & 85.99 \% & 70.53 \% & 0.05 s / 1 core & 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.\\
Fast Point R-CNN & la & 77.40 \% & 85.29 \% & 70.24 \% & 0.06 s / GPU & Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.\\
3D\_att & la & 77.27 \% & 88.46 \% & 70.11 \% & 0.17 s / GPU & \\
Patches & la & 77.20 \% & 88.67 \% & 71.82 \% & 0.15 s / GPU & 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.\\
Sem-Aug-PointRCNN & & 77.04 \% & 82.75 \% & 73.21 \% & 0.1 s / GPU & \\
HRI-VoxelFPN & & 76.70 \% & 85.64 \% & 69.44 \% & 0.02 s / GPU & 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.\\
SARPNET & & 76.64 \% & 85.63 \% & 71.31 \% & 0.05 s / 1 core & 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.\\
YF & & 76.57 \% & 87.15 \% & 71.23 \% & 0.04 s / GPU & \\
3D IoU Loss & la & 76.50 \% & 86.16 \% & 71.39 \% & 0.08 s / GPU & 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.\\
HNet-3DSSD & la & 76.48 \% & 86.06 \% & 69.71 \% & 0.05 s / GPU & \\
F-ConvNet & la & 76.39 \% & 87.36 \% & 66.69 \% & 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.\\
DPointNet & & 76.34 \% & 81.67 \% & 70.34 \% & 0.07s / 1 core & \\
SegVoxelNet & & 76.13 \% & 86.04 \% & 70.76 \% & 0.04 s / 1 core & 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.\\
S-AT GCN & & 76.04 \% & 83.20 \% & 71.17 \% & 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.\\
TANet & & 75.94 \% & 84.39 \% & 68.82 \% & 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.\\
APL-Second & & 75.75 \% & 84.26 \% & 70.65 \% & 0.05 s / 1 core & \\
PointRGCN & & 75.73 \% & 85.97 \% & 70.60 \% & 0.26 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
MMLab-PointRCNN & la & 75.64 \% & 86.96 \% & 70.70 \% & 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.\\
AB3DMOT & la on & 75.43 \% & 86.10 \% & 68.88 \% & 0.0047s / 1 core & X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.\\
R-GCN & & 75.26 \% & 83.42 \% & 68.73 \% & 0.16 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
epBRM & la & 75.15 \% & 85.00 \% & 69.84 \% & 0.1 s / GPU & K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.\\
MAFF-Net(DAF-Pillar) & & 75.04 \% & 85.52 \% & 67.61 \% & 0.04 s / 1 core & 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.\\
sscl-20p & & 74.82 \% & 86.06 \% & 69.87 \% & 0.02 s / 1 core & \\
PI-RCNN & & 74.82 \% & 84.37 \% & 70.03 \% & 0.1 s / 1 core & 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.\\
FPGNN & & 74.77 \% & 83.82 \% & 67.93 \% & 0.05 s / 1 core & \\
FPC3D\_all & la & 74.55 \% & 85.50 \% & 69.91 \% & 0.03 s / 1 core & \\
PointPillars & la & 74.31 \% & 82.58 \% & 68.99 \% & 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.\\
ARPNET & & 74.04 \% & 84.69 \% & 68.64 \% & 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.\\
PC-CNN-V2 & la & 73.79 \% & 85.57 \% & 65.65 \% & 0.5 s / GPU & 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.\\
C-GCN & & 73.62 \% & 83.49 \% & 67.01 \% & 0.147 s / GPU & J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.\\
LSNet & & 73.55 \% & 86.13 \% & 68.58 \% & 0.09 s / GPU & \\
3DBN & la & 73.53 \% & 83.77 \% & 66.23 \% & 0.13s / & X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.\\
PointRGBNet & & 73.49 \% & 83.99 \% & 68.56 \% & 0.08 s / 4 cores & \\
RangeDet & & 73.44 \% & 80.53 \% & 67.28 \% & 0.01 s / 1 core & \\
SCNet & la & 73.17 \% & 83.34 \% & 67.93 \% & 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.\\
PP-PCdet & & 73.07 \% & 83.32 \% & 68.18 \% & 0.01 s / 1 core & \\
PFF3D & la & 72.93 \% & 81.11 \% & 67.24 \% & 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.\\
DASS & & 72.31 \% & 81.85 \% & 65.99 \% & 0.09 s / 1 core & 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.\\
HS3D & & 72.25 \% & 83.57 \% & 67.49 \% & 0.12 s / 1 core & \\
TBD\_BD & & 72.16 \% & 83.36 \% & 66.87 \% & 0.03 s / 1 core & \\
AVOD-FPN & la & 71.76 \% & 83.07 \% & 65.73 \% & 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.\\
PointPainting & la & 71.70 \% & 82.11 \% & 67.08 \% & 0.4 s / GPU & S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.\\
Contrastive PP & & 71.64 \% & 84.80 \% & 66.49 \% & 0.01 s / 1 core & \\
WS3D & la & 70.59 \% & 80.99 \% & 64.23 \% & 0.1 s / GPU & Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.\\
F-PointNet & la & 69.79 \% & 82.19 \% & 60.59 \% & 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.\\
FusionDetv2-baseline & & 68.87 \% & 79.05 \% & 63.68 \% & 0.04 s / 1 core & \\
UberATG-ContFuse & la & 68.78 \% & 83.68 \% & 61.67 \% & 0.06 s / GPU & M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.\\
MLOD & la & 67.76 \% & 77.24 \% & 62.05 \% & 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.\\
DSGN++ & & 67.37 \% & 83.21 \% & 59.91 \% & 0.4 s / 1 core & \\
AVOD & la & 66.47 \% & 76.39 \% & 60.23 \% & 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.\\
FusionDetv2-v1 & & 65.65 \% & 75.21 \% & 60.65 \% & 0.04 s / 1 core & \\
MMLAB LIGA-Stereo & st & 64.66 \% & 81.39 \% & 57.22 \% & 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.\\
BirdNet+ & la & 64.04 \% & 76.15 \% & 59.79 \% & 0.11 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.\\
MV3D & la & 63.63 \% & 74.97 \% & 54.00 \% & 0.36 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
KMC & & 62.74 \% & 74.45 \% & 56.76 \% & 0.05 s / 1 core & \\
LIGA-Stereo-old & st & 62.65 \% & 81.76 \% & 55.24 \% & 0.375 s / & \\
SD3DOD & & 62.00 \% & 76.09 \% & 55.46 \% & 0.04 s / GPU & \\
AEC3D & & 61.99 \% & 72.16 \% & 57.11 \% & 18 ms / GPU & \\
VN3D & & 61.41 \% & 72.37 \% & 56.86 \% & 0.02 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
RCD & & 60.56 \% & 70.54 \% & 55.58 \% & 0.1 s / GPU & 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.\\
deleted & & 57.11 \% & 76.87 \% & 50.05 \% & 0.3 s / GPU & \\
A3DODWTDA & la & 56.82 \% & 62.84 \% & 48.12 \% & 0.08 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
PL++ (SDN+GDC) & st la & 54.88 \% & 68.38 \% & 49.16 \% & 0.6 s / GPU & 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.\\
MV3D (LIDAR) & la & 54.54 \% & 68.35 \% & 49.16 \% & 0.24 s / GPU & X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.\\
CDN & st & 54.22 \% & 74.52 \% & 46.36 \% & 0.6 s / GPU & 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.\\
CG-Stereo & st & 53.58 \% & 74.39 \% & 46.50 \% & 0.57 s / & C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.\\
DSGN & st & 52.18 \% & 73.50 \% & 45.14 \% & 0.67 s / & Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.\\
BirdNet+ (legacy) & la & 51.85 \% & 70.14 \% & 50.03 \% & 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.\\
ppt & & 50.41 \% & 54.19 \% & 45.14 \% & 0.1 s / 1 core & \\
NCL & & 50.07 \% & 46.58 \% & 50.33 \% & NA s / 1 core & \\
SOD & & 48.69 \% & 70.90 \% & 40.12 \% & 0.1 s / 1 core & \\
Complexer-YOLO & la & 47.34 \% & 55.93 \% & 42.60 \% & 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.\\
EGFN & st & 46.39 \% & 65.80 \% & 38.42 \% & 0.06 s / GPU & \\
Disp R-CNN (velo) & st & 45.78 \% & 68.21 \% & 37.73 \% & 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.\\
CDN-PL++ & st & 44.86 \% & 64.31 \% & 38.11 \% & 0.4 s / GPU & 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.\\
Disp R-CNN & st & 43.27 \% & 67.02 \% & 36.43 \% & 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.\\
R-AGNO-Net & & 42.79 \% & 49.49 \% & 39.31 \% & 0.15 s / 1 core & \\
Pseudo-LiDAR++ & st & 42.43 \% & 61.11 \% & 36.99 \% & 0.4 s / GPU & 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.\\
OSE+ & & 41.60 \% & 62.67 \% & 35.82 \% & 0.1 s / 1 core & \\
YOLOStereo3D & st & 41.25 \% & 65.68 \% & 30.42 \% & 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.\\
BEVC & & 40.72 \% & 50.05 \% & 36.42 \% & 35ms / GPU & \\
RT3D-GMP & st & 38.76 \% & 45.79 \% & 30.00 \% & 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.\\
ZoomNet & st & 38.64 \% & 55.98 \% & 30.97 \% & 0.3 s / 1 core & L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.\\
OC Stereo & st & 37.60 \% & 55.15 \% & 30.25 \% & 0.35 s / 1 core & A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.\\
Pseudo-Lidar & st & 34.05 \% & 54.53 \% & 28.25 \% & 0.4 s / GPU & 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.\\
Stereo R-CNN & st & 30.23 \% & 47.58 \% & 23.72 \% & 0.3 s / GPU & P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.\\
BirdNet & la & 27.26 \% & 40.99 \% & 25.32 \% & 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.\\
TBD & & 24.87 \% & 33.30 \% & 21.96 \% & 0.1 s / 1 core & \\
RT3DStereo & st & 23.28 \% & 29.90 \% & 18.96 \% & 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.\\
Digging\_M3D & & 21.24 \% & 29.15 \% & 19.18 \% & 0.03 s / 1 core & \\
RT3D & la & 19.14 \% & 23.74 \% & 18.86 \% & 0.09 s / GPU & 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.\\
Mix-Teaching-M3D & & 18.54 \% & 26.89 \% & 15.79 \% & 0.03 s / 1 core & \\
StereoFENet & st & 18.41 \% & 29.14 \% & 14.20 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
CMKD & & 18.00 \% & 28.10 \% & 15.60 \% & 0.1 s / 1 core & \\
SCSTSV-MonoFlex & & 17.91 \% & 27.38 \% & 15.58 \% & 0.03 s / 1 core & \\
LPCG-Monoflex & & 17.80 \% & 25.56 \% & 15.38 \% & 0.03 s / 1 core & \\
PS-fld & & 17.74 \% & 23.74 \% & 15.14 \% & 0.25 s / 1 core & \\
MonoDDE & & 17.14 \% & 24.93 \% & 15.10 \% & 0.04 s / 1 core & \\
Mobile Stereo R-CNN & st & 17.04 \% & 26.97 \% & 13.26 \% & 1.8 s / & 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.\\
CMKD & & 16.99 \% & 27.20 \% & 15.08 \% & 0.1 s / 1 core & \\
MonoCon & & 16.46 \% & 22.50 \% & 13.95 \% & 0.02 s / GPU & \\
DD3D & & 16.34 \% & 23.22 \% & 14.20 \% & 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) .\\
gupnet\_se & & 16.10 \% & 23.62 \% & 13.41 \% & 0.03s / 1 core & \\
MonoDistill & & 16.03 \% & 22.97 \% & 13.60 \% & 0.04 s / 1 core & \\
ZongmuMono3dV2 & & 15.73 \% & 23.96 \% & 13.35 \% & 0.08 s / 1 core & \\
MonoDTR & & 15.39 \% & 21.99 \% & 12.73 \% & 0.04 s / 1 core & \\
mono3d & & 15.26 \% & 23.41 \% & 12.80 \% & 0.03 s / GPU & \\
ZongmuMono3d & & 15.08 \% & 23.79 \% & 13.25 \% & 0.08 s / 1 core & \\
GUPNet & & 15.02 \% & 22.26 \% & 13.12 \% & 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.\\
vadin-TBD & & 14.94 \% & 21.75 \% & 13.07 \% & 0.04 s / 1 core & \\
LPCG-M3D & & 14.82 \% & 22.73 \% & 12.88 \% & 0.11 s / 1 core & \\
M3DSSD++ & & 14.75 \% & 23.61 \% & 11.80 \% & 0.16s / 1 core & \\
MonoFlex & & 14.73 \% & 22.29 \% & 12.77 \% & 0.03 s / 1 core & \\
SGM3D & & 14.65 \% & 22.46 \% & 12.97 \% & 0.03 s / 1 core & \\
Anonymous & & 14.56 \% & 20.65 \% & 11.92 \% & 0.09 s / 1 core & \\
SAIC\_ADC\_Mono3D & & 14.54 \% & 18.98 \% & 12.86 \% & 50 s / GPU & \\
EW & & 14.50 \% & 23.37 \% & 11.88 \% & 0.05 s / 1 core & \\
CA3D & & 14.49 \% & 20.89 \% & 12.19 \% & 0.04 s / 1 core & \\
MonoEdge & & 14.47 \% & 21.08 \% & 12.73 \% & 0.05 s / GPU & \\
MonoGround & & 14.36 \% & 21.37 \% & 12.62 \% & 0.03 s / 1 core & \\
ANM & & 14.33 \% & 20.84 \% & 11.61 \% & 0.02 s / 1 core & \\
DLE & & 14.33 \% & 24.23 \% & 10.30 \% & 0.06 s / & 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.\\
ITS-MDPL & & 14.28 \% & 24.67 \% & 12.13 \% & 0.16 s / GPU & \\
SwinMono3D & & 14.24 \% & 22.61 \% & 10.11 \% & 0.08 s / 1 core & \\
AutoShape & & 14.17 \% & 22.47 \% & 11.36 \% & 0.04 s / 1 core & 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.\\
MonoEdge-Rotate & & 14.13 \% & 21.60 \% & 12.27 \% & 0.05 s / GPU & \\
EM & & 14.00 \% & 22.93 \% & 11.26 \% & 0.05 s / 1 core & \\
MAOLoss & & 14.00 \% & 20.05 \% & 11.81 \% & 0.05 s / 1 core & \\
E2E-DA & & 13.97 \% & 19.73 \% & 11.82 \% & 0.03 s / 1 core & \\
GAC3D++ & & 13.90 \% & 19.53 \% & 11.77 \% & 0.25 s / 1 core & \\
MonoFlex & & 13.89 \% & 19.94 \% & 12.07 \% & 0.03 s / GPU & Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.\\
MonoEF & & 13.87 \% & 21.29 \% & 11.71 \% & 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.\\
MonoGeo & & 13.81 \% & 18.85 \% & 11.52 \% & 0.05 s / 1 core & \\
K3D & & 13.80 \% & 20.04 \% & 11.67 \% & 0.3 s / 1 core & \\
none & & 13.79 \% & 18.84 \% & 11.52 \% & 0.03 s / 1 core & \\
DFR-Net & & 13.63 \% & 19.40 \% & 10.35 \% & 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.\\
MonoLCD & & 13.52 \% & 18.08 \% & 11.58 \% & 0.04 s / 1 core & \\
CaDDN & & 13.41 \% & 19.17 \% & 11.46 \% & 0.63 s / GPU & C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.\\
MDSNet & & 13.40 \% & 22.80 \% & 10.27 \% & 0.07 s / 1 core & \\
PCT & & 13.37 \% & 21.00 \% & 11.31 \% & 0.045 s / 1 core & 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.\\
KAIST-VDCLab & & 13.33 \% & 19.06 \% & 11.90 \% & 0.04 s / 1 core & \\
Ground-Aware & & 13.25 \% & 21.65 \% & 9.91 \% & 0.05 s / 1 core & Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.\\
MonoHMOO & & 13.12 \% & 20.28 \% & 9.56 \% & 0.2 s / 1 core & \\
Aug3D-RPN & & 12.99 \% & 17.82 \% & 9.78 \% & 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.\\
vadin-TBD2 & & 12.99 \% & 20.10 \% & 10.50 \% & 0.20 s / 1 core & \\
RelationNet3D\_dla34 & & 12.88 \% & 17.67 \% & 11.01 \% & 0.04 s / 1 core & \\
PLDet3d & & 12.85 \% & 20.72 \% & 11.11 \% & 0.11 s / 1 core & \\
DDMP-3D & & 12.78 \% & 19.71 \% & 9.80 \% & 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.\\
RetinaMono & & 12.73 \% & 19.41 \% & 10.45 \% & 0.02 s / 1 core & \\
Kinematic3D & & 12.72 \% & 19.07 \% & 9.17 \% & 0.12 s / 1 core & G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .\\
MonoRCNN & & 12.65 \% & 18.36 \% & 10.03 \% & 0.07 s / GPU & X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.\\
RelationNet3D & & 12.60 \% & 17.57 \% & 10.95 \% & 0.04 s / GPU & \\
TBD & & 12.53 \% & 22.40 \% & 10.64 \% & 0.3 s / 1 core & \\
AutoShape & & 12.42 \% & 20.35 \% & 9.70 \% & 0.04 s / 1 core & \\
MP-Mono & & 12.37 \% & 17.89 \% & 9.58 \% & 0.16 s / GPU & \\
GrooMeD-NMS & & 12.32 \% & 18.10 \% & 9.65 \% & 0.12 s / 1 core & A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.\\
MonoRUn & & 12.30 \% & 19.65 \% & 10.58 \% & 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.\\
monodle & & 12.26 \% & 17.23 \% & 10.29 \% & 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 .\\
PPTrans & & 12.06 \% & 19.79 \% & 10.48 \% & 0.2 s / GPU & \\
YoloMono3D & & 12.06 \% & 18.28 \% & 8.42 \% & 0.05 s / GPU & 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.\\
IAFA & & 12.01 \% & 17.81 \% & 10.61 \% & 0.04 s / 1 core & 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.\\
GAC3D & & 12.00 \% & 17.75 \% & 9.15 \% & 0.25 s / 1 core & M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.\\
PGD-FCOS3D & & 11.76 \% & 19.05 \% & 9.39 \% & 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.\\
D4LCN & & 11.72 \% & 16.65 \% & 9.51 \% & 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.\\
RetinaMono & & 11.61 \% & 16.68 \% & 9.57 \% & 0.02 s / 1 core & \\
KM3D & & 11.45 \% & 16.73 \% & 9.92 \% & 0.03 s / 1 core & P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.\\
RefinedMPL & & 11.14 \% & 18.09 \% & 8.94 \% & 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.\\
PatchNet & & 11.12 \% & 15.68 \% & 10.17 \% & 0.4 s / 1 core & 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.\\
ImVoxelNet & & 10.97 \% & 17.15 \% & 9.15 \% & 0.2 s / GPU & 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.\\
COF3D & & 10.91 \% & 17.86 \% & 8.20 \% & 200 s / 1 core & \\
MM & & 10.74 \% & 15.80 \% & 8.64 \% & 1 s / 1 core & \\
AM3D & & 10.74 \% & 16.50 \% & 9.52 \% & 0.4 s / GPU & 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.\\
Lite-FPN & & 10.64 \% & 15.32 \% & 8.59 \% & 0.01 s / 1 core & \\
TBD & & 10.61 \% & 15.71 \% & 8.66 \% & 0.1 s / 1 core & \\
Keypoint-3D & & 10.42 \% & 15.97 \% & 7.91 \% & 14 s / 1 core & \\
RTM3D & & 10.34 \% & 14.41 \% & 8.77 \% & 0.05 s / GPU & P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.\\
E2E-DA-Lite (Res18) & & 10.32 \% & 15.56 \% & 8.89 \% & 0.01 s / GPU & \\
MonoPair & & 9.99 \% & 13.04 \% & 8.65 \% & 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.\\
RelationNet3D\_res18 & & 9.93 \% & 14.27 \% & 8.43 \% & 0.04 s / 1 core & \\
Neighbor-Vote & & 9.90 \% & 15.57 \% & 8.89 \% & 0.1 s / GPU & 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.\\
SMOKE & & 9.76 \% & 14.03 \% & 7.84 \% & 0.03 s / GPU & Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.\\
M3D-RPN & & 9.71 \% & 14.76 \% & 7.42 \% & 0.16 s / GPU & G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .\\
QD-3DT & on & 9.33 \% & 12.81 \% & 7.86 \% & 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.\\
ICCV & & 9.31 \% & 13.37 \% & 8.29 \% & 0.04 s / GPU & \\
TopNet-HighRes & la & 9.28 \% & 12.67 \% & 7.95 \% & 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.\\
MonoCInIS & & 7.94 \% & 15.82 \% & 6.68 \% & 0,13 s / GPU & 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.\\
Geo3D & & 7.70 \% & 11.52 \% & 6.80 \% & 0.04 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
SS3D & & 7.68 \% & 10.78 \% & 6.51 \% & 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.\\
MonoCInIS & & 7.66 \% & 15.21 \% & 6.24 \% & 0,14 s / GPU & 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.\\
Mono3D\_PLiDAR & & 7.50 \% & 10.76 \% & 6.10 \% & 0.1 s / & X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.\\
MonoPSR & & 7.25 \% & 10.76 \% & 5.85 \% & 0.2 s / GPU & J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.\\
Decoupled-3D & & 7.02 \% & 11.08 \% & 5.63 \% & 0.08 s / GPU & 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.\\
VoxelJones & & 6.35 \% & 7.39 \% & 5.80 \% & .18 s / 1 core & M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.\\
MonoGRNet & & 5.74 \% & 9.61 \% & 4.25 \% & 0.04s / & 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.\\
A3DODWTDA (image) & & 5.27 \% & 6.88 \% & 4.45 \% & 0.8 s / GPU & F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.\\
MonoFENet & & 5.14 \% & 8.35 \% & 4.10 \% & 0.15 s / 1 core & W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.\\
TLNet (Stereo) & st & 4.37 \% & 7.64 \% & 3.74 \% & 0.1 s / 1 core & 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.\\
CSoR & la & 4.06 \% & 5.61 \% & 3.17 \% & 3.5 s / 4 cores & L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.\\
Shift R-CNN (mono) & & 3.87 \% & 6.88 \% & 2.83 \% & 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.\\
MVRA + I-FRCNN+ & & 3.27 \% & 5.19 \% & 2.49 \% & 0.18 s / GPU & H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.\\
SparVox3D & & 3.20 \% & 5.27 \% & 2.56 \% & 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.\\
TopNet-UncEst & la & 3.02 \% & 3.24 \% & 2.26 \% & 0.09 s / & S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.\\
GS3D & & 2.90 \% & 4.47 \% & 2.47 \% & 2 s / 1 core & 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.\\
3D-GCK & & 2.52 \% & 3.27 \% & 2.11 \% & 24 ms / & 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.\\
WeakM3D & & 2.26 \% & 5.03 \% & 1.63 \% & 0.08 s / 1 core & \\
ROI-10D & & 2.02 \% & 4.32 \% & 1.46 \% & 0.2 s / GPU & 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.\\
CDTrack3D & & 1.65 \% & 3.01 \% & 1.40 \% & 0.0106 s / & \\
FQNet & & 1.51 \% & 2.77 \% & 1.01 \% & 0.5 s / 1 core & 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.\\
3D-SSMFCNN & & 1.41 \% & 1.88 \% & 1.11 \% & 0.1 s / GPU & L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.\\
mBoW & la & 0.00 \% & 0.00 \% & 0.00 \% & 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.
\end{tabular}