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-# Monocular3DDetection
+# Awesome Monocular 3D Detection
+Paper list of 3D detetction, keep updating!
+
+## Contents
+- [Paper List](#Paper-List)
+	- [2024](#2024)
+   	- [2023](#2023)
+    - [2022](#2022)
+    - [2021](#2021)
+    - [2020](#2020)
+    - [2019](#2019)
+    - [2018](#2018)
+    - [2017](#2017)
+    - [2016](#2016)
+- [KITTI Results](#KITTI-Results)
+
+# Paper List
+## 2024
+- <a id="MonoWAD"></a>**[MonoWAD]** MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection [[ECCV2024](https://arxiv.org/pdf/2407.16448)][[Pytorch](https://github.com/VisualAIKHU/MonoWAD)]
+- <a id="MonoTTA"></a>**[MonoTTA]** Fully Test-Time Adaptation for Monocular 3D Object Detection [[ECCV2024](https://arxiv.org/pdf/2405.19682)][[Pytorch](https://github.com/Hongbin98/MonoTTA)]
+- <a id="MonoMAE"></a>**[MonoMAE]** MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders [[NeurIPS2024](https://arxiv.org/pdf/2405.07696)]
+- <a id="OVM3D"></a>**[OVM3D]** Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data [[NeurIPS2024](https://arxiv.org/pdf/2411.15657)]
+- <a id="MonoCD"></a>**[MonoCD]** MonoCD: Monocular 3D Object Detection with Complementary Depths [[CVPR2024](https://arxiv.org/pdf/2404.03181)][[Pytorch](https://github.com/elvintanhust/MonoCD)]
+- <a id="DPL"></a>**[DPL]** Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2403.17387)]
+- <a id="UniMODE"></a>**[UniMODE]** UniMODE: Unified Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2402.18573)]
+- <a id="YOLOBU"></a>**[YOLOBU]** You Only Look Bottom-Up for Monocular 3D Object Detection [[RA-L2024](https://arxiv.org/pdf/2401.15319)]
+
+## 2023
+- <a id="DDML"></a>**[DDML]** Depth-discriminative Metric Learning for Monocular 3D Object Detection [[NeurIPS2023](https://arxiv.org/pdf/2401.01075.pdf)]
+- <a id="MonoXiver"></a>**[MonoXiver]** Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver [[ICCV2023](https://arxiv.org/pdf/2304.01289.pdf)]
+- <a id="MonoNeRD"></a>**[MonoNeRD]** MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection [[ICCV2023](https://arxiv.org/pdf/2308.09421.pdf)][[Pytorch](https://github.com/cskkxjk/MonoNeRD)]
+- <a id="MonoATT"></a>**[MonoATT]** MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer [[CVPR2023](https://arxiv.org/abs/2303.13018)]
+- <a id="WeakMono3D"></a>**[WeakMono3D]** Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [[CVPR2023](https://arxiv.org/pdf/2303.08686.pdf)]
+- <a id="MonoPGC"></a>**[MonoPGC]** MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts [[ICRA2023](https://arxiv.org/pdf/2302.10549.pdf)]
+- <a id="ADD"></a>**[ADD]** Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection[[AAAI2023](https://arxiv.org/pdf/2211.16779.pdf)]
+
+## 2022
+- <a id="MoGDE"></a>**[MoGDE]** MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation [[NeurIPS2022](https://arxiv.org/abs/2303.13561)]
+- <a id="LPCG"></a>**[LPCG]** Lidar Point Cloud Guided Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/abs/2104.09035)][[Pytorch](https://github.com/SPengLiang/LPCG)]
+- <a id="MVC-MonoDet"></a>**[MVC-MonoDet]** Semi-Supervised Monocular 3D Object Detection by Multi-View Consistency [[ECCV2022](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136680702.pdf)][[Pytorch](https://github.com/lianqing11/mvc_monodet)]
+- <a id="CMKD"></a>**[CMKD]** Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/abs/2211.07171)][[Pytorch](https://github.com/Cc-Hy/CMKD)]
+- <a id="DfM"></a>**[DfM]**  Monocular 3D Object Detection with Depth from Motion [[ECCV2022](https://arxiv.org/pdf/2207.12988.pdf)][[Pytorch](https://github.com/Tai-Wang/Depth-from-Motion)]
+- <a id="DEVIANT"></a>**[DEVIANT]** DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/pdf/2207.10758.pdf)][[Pytorch](https://github.com/abhi1kumar/DEVIANT)]
+- <a id="DCD"></a>**[DCD]** Densely Constrained Depth Estimator for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/pdf/2207.10047.pdf)][[Pytorch](https://github.com/BraveGroup/DCD)]
+- <a id="STMono3D"></a>**[STMono3D]** Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [[ECCV2022](https://arxiv.org/pdf/2204.11590.pdf)]
+- <a id="DID-M3D"></a>**[DID-M3D]** DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/pdf/2207.08531.pdf)][[Pytorch](https://github.com/SPengLiang/DID-M3D)]
+- <a id="SGM3D"></a>**[SGM3D]** SGM3D: Stereo Guided Monocular 3D Object Detection [[RA-L2022](https://arxiv.org/pdf/2112.01914.pdf)][[Pytorch](https://github.com/zhouzheyuan/sgm3d)]
+- <a id="PRT"></a>**[PRT]** Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking [[ICRA2022](https://arxiv.org/pdf/2206.03666.pdf)]
+- <a id="Time3D"></a>**[Time3D]** Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving [[CVPR2022](https://arxiv.org/pdf/2205.14882.pdf)]
+- <a id="MonoGround"></a>**[MonoGround]** MonoGround: Detecting Monocular 3D Objects from the Ground [[CVPR2022](https://arxiv.org/pdf/2206.07372.pdf)][[Pytorch](https://github.com/cfzd/MonoGround)]
+- <a id="DimEmbedding"></a>**[DimEmbedding]** Dimension Embeddings for Monocular 3D Object Detection [[CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Dimension_Embeddings_for_Monocular_3D_Object_Detection_CVPR_2022_paper.pdf)]
+- <a id="GeoAug"></a>**[GeoAug]** Exploring Geometric Consistency for Monocular 3D Object Detection [[CVPR2022](https://openaccess.thecvf.com/content/CVPR2022/papers/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.pdf)]
+- <a id='MonoDDE'></a>**[MonoDDE]** Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection [[CVPR2022](https://arxiv.org/pdf/2205.09373.pdf)]
+- <a id='Homography'></a>**[Homography]** Homography Loss for Monocular 3D Object Detection [[CVPR2022](https://arxiv.org/pdf/2204.00754.pdf)]
+- <a id='Rope3D'></a>**[Rope3D]** Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task [[CVPR2022](https://arxiv.org/pdf/2203.13608.pdf)][[Pytorch](https://github.com/liyingying0113/rope3d-dataset-tools)]
+- <a id='MonoDTR'></a>**[MonoDTR]** MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer [[CVPR2022](https://arxiv.org/pdf/2203.10981.pdf)][[Pytorch](https://github.com/kuanchihhuang/MonoDTR)]
+- <a id='MonoJSG'></a>**[MonoJSG]** MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection [[CVPR2022](https://arxiv.org/pdf/2203.08563.pdf)][[Pytorch](https://github.com/lianqing11/MonoJSG)]
+- <a id='Pseudo-Stereo'></a>**[Pseudo-Stereo]** Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving [[CVPR2022](https://arxiv.org/pdf/2203.02112.pdf)][[Pytorch](https://github.com/revisitq/Pseudo-Stereo-3D)]
+- <a id='MonoDistill'></a>**[MonoDistill]** MonoDistill: Learning Spatial Features for Monocular 3D Object Detection [[ICLR2022](https://arxiv.org/pdf/2201.10830.pdf)][[Pytorch](https://github.com/monster-ghost/MonoDistill)]
+- <a id='WeakM3D'></a>**[WeakM3D]** WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection [[ICLR2022](https://openreview.net/pdf?id=ahi2XSHpAUZ)][[Pytorch](https://github.com/SPengLiang/WeakM3D)]
+- <a id='MonoCon'></a>**[MonoCon]** Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection [[AAAI2022](https://arxiv.org/pdf/2112.04628.pdf)][[Pytorch](https://github.com/Xianpeng919/MonoCon)]
+- <a id='ImVoxelNet'></a>**[ImVoxelNet]** ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection [[WACV2022](https://arxiv.org/pdf/2106.01178.pdf)][[Pytorch](https://github.com/saic-vul/imvoxelnet)]
+
+
+## 2021
+- <a id="PCT"></a>**[PCT]** Progressive Coordinate Transforms for Monocular 3D Object Detection [[NeurIPS2021](https://arxiv.org/pdf/2108.05793.pdf)][[Pytorch](https://github.com/amazon-research/progressive-coordinate-transforms)]
+- <a id="DeepLineEncoding"></a>**[DeepLineEncoding]** Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction [[BMVC2021](https://www.bmvc2021-virtualconference.com/assets/papers/0299.pdf)][[Pytorch](https://github.com/cnexah/DeepLineEncoding)]
+- <a id="DFR-Net"></a>**[DFR-Net]** The Devil Is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [[ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/html/Zou_The_Devil_Is_in_the_Task_Exploiting_Reciprocal_Appearance-Localization_Features_ICCV_2021_paper.html)]
+- <a id="AutoShape"></a>**[AutoShape]** AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [[ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_AutoShape_Real-Time_Shape-Aware_Monocular_3D_Object_Detection_ICCV_2021_paper.pdf)][[Pytorch](https://github.com/zongdai/AutoShape)][[Paddle](https://github.com/zongdai/AutoShape)]
+- <a id="analysis"></a>**[pseudo-analysis]** Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection? [[ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Simonelli_Are_We_Missing_Confidence_in_Pseudo-LiDAR_Methods_for_Monocular_3D_ICCV_2021_paper.pdf)]
+- <a id="Gated3D"></a>**[Gated3D]** Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues [[ICCV2021](https://openaccess.thecvf.com/content/ICCV2021/papers/Julca-Aguilar_Gated3D_Monocular_3D_Object_Detection_From_Temporal_Illumination_Cues_ICCV_2021_paper.pdf)]
+- <a id="MonoRCNN"></a>**[MonoRCNN]** Geometry-based Distance Decomposition for Monocular 3D Object Detection [[ICCV2021](https://arxiv.org/abs/2104.03775)][[Pytorch](https://github.com/Rock-100/MonoDet)]
+- <a id="DD3D"></a>**[DD3D]** Is Pseudo-Lidar needed for Monocular 3D Object detection [[ICCV2021](https://arxiv.org/pdf/2108.06417.pdf)][[Pytorch](https://github.com/tri-ml/dd3d)]
+- <a id="GUPNet"></a>**[GUPNet]** Geometry Uncertainty Projection Network for Monocular 3D Object Detection [[ICCV2021](https://arxiv.org/pdf/2107.13774.pdf)][[Pytorch](https://github.com/SuperMHP/GUPNet)]
+- <a id="neighbor-voting"></a>**[Neighbor-Vote]** Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting [[ACMMM2021](https://arxiv.org/pdf/2107.02493.pdf)][[Pytorch](https://github.com/cxmomo/Neighbor-Vote)]
+- <a id="MonoEF"></a>**[MonoEF]** Monocular 3D Object Detection: An Extrinsic Parameter Free Approach [[CVPR2021](https://arxiv.org/abs/2106.15796?context=cs)][[Pytorch](https://github.com/ZhouYunsong-SJTU/MonoEF)]
+- <a id="monodle"></a>**[monodle]** Delving into Localization Errors for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.16237)][[Pytorch](https://github.com/xinzhuma/monodle)]
+- <a id="Monoflex"></a>**[Monoflex]** Objects are Different: Flexible Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2104.02323)][[Pytorch](https://github.com/zhangyp15/MonoFlex)]
+- <a id="GrooMeD-NMS"></a>**[GrooMeD-NMS]** GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.17202)][[Pytorch](https://github.com/abhi1kumar/groomed_nms)]
+- <a id="DDMP-3D"></a>**[DDMP-3D]** Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.16470)][[Pytorch](https://github.com/Willy0919/DDMP-3D)]
+- <a id="MonoRUn"></a>**[MonoRUn]** MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation [[CVPR2021](https://arxiv.org/abs/2103.12605)][[Pytorch](https://github.com/tjiiv-cprg/MonoRUn)]
+- <a id="M3DSSD"></a>**[M3DSSD]** M3DSSD: Monocular 3D Single Stage Object Detector [[CVPR2021](https://arxiv.org/abs/2103.13164)][[Pytorch](https://github.com/mumianyuxin/M3DSSD)]
+- <a id="CaDDN"></a>**[CaDDN]** Categorical Depth Distribution Network for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.01100)][[Pytorch](https://github.com/TRAILab/CaDDN)]
+- <a id="visualDet3D"></a>**[visualDet3D]** Ground-aware Monocular 3D Object Detection for Autonomous Driving [[RA-L](https://arxiv.org/abs/2102.00690)][[Pytorch](https://github.com/Owen-Liuyuxuan/visualDet3D)]
+ 
+## 2020
+- <a name="UR3D"></a>**[UR3D]** Distance-Normalized Unified Representation for Monocular 3D Object Detection [[ECCV2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740086.pdf)]
+- <a name="MonoDR"></a>**[MonoDR]** Monocular Differentiable Rendering for Self-Supervised 3D Object Detection [[ECCV2020](https://arxiv.org/abs/2009.14524)]
+- <a id="DA-3Ddet"></a>**[DA-3Ddet]** Monocular 3d object detection via feature domain adaptation [[ECCV2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540018.pdf)]
+- <a id="MoVi-3D"></a>**[MoVi-3D]** Towards generalization across depth for monocular 3d object detection [[ECCV2020](https://arxiv.org/abs/1912.08035)]
+- <a id="PatchNet"></a>**[PatchNet]** Rethinking Pseudo-LiDAR Representation [[ECCV2020](https://arxiv.org/abs/2008.04582)][[Pytorch](https://github.com/xinzhuma/patchnet)]
+- <a id="RAR-Net"></a>**[RAR-Net]** Reinforced Axial Refinement Network for Monocular 3D Object Detection [[ECCV2020](https://arxiv.org/abs/2008.13748)]
+- <a id='kinematic3d'></a>**[kinematic3d]** Kinematic 3D Object Detection in Monocular Video [[ECCV2020](https://arxiv.org/abs/2007.09548)][[Pytorch](https://github.com/garrickbrazil/kinematic3d)]
+- <a id="RTM3D"></a>**[RTM3D]** RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving [[ECCV2020](https://arxiv.org/abs/2001.03343)][[Pytorch](https://github.com/Banconxuan/RTM3D)]
+- <a id="SMOKE"></a>**[SMOKE]** SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation [[CVPRW2020](https://arxiv.org/pdf/2002.10111.pdf)][[Pytorch](https://github.com/lzccccc/SMOKE)]
+- <a id="D4LCN"></a>**[D4LCN]** Learning Depth-Guided Convolutions for Monocular 3D Object Detection [[CVPRW2020](https://arxiv.org/abs/1912.04799)][[Pytorch](https://github.com/dingmyu/D4LCN)]
+- <a id="MonoPair"></a>**[MonoPair]** MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships [[CVPR2020](https://arxiv.org/abs/2003.00504)]
+- <a id="pseudo-LiDAR_e2e"></a>**[pseudo-LiDAR_e2e]** End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection [[CVPR2020](https://arxiv.org/abs/2004.03080)][[Pytorch](https://github.com/mileyan/pseudo-LiDAR_e2e)]
+- <a id="Pseudo-LiDAR++"></a>**[Pseudo-LiDAR++]** Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving [[ICLR2020](https://arxiv.org/abs/1906.06310)][[Pytorch](https://github.com/mileyan/Pseudo_Lidar_V2)]
+- <a id="OACV"></a>**[OACV]** Object-Aware Centroid Voting for Monocular 3D Object Detection [[IROS2020](https://arxiv.org/abs/2007.09836)]
+- <a id="MonoGRNet_v2"></a>**[MonoGRNet_v2]** Monocular 3D Object Detection via Geometric Reasoning on Keypoints [[VISIGRAPP2020](https://arxiv.org/abs/1905.05618)]
+- <a id="ForeSeE"></a>**[ForeSeE]** Task-Aware Monocular Depth Estimation for 3D Object Detection [[AAAI2020(oral)](https://arxiv.org/abs/1909.07701)][[Pytorch](https://github.com/WXinlong/ForeSeE)]
+- <a id="Decoupled-3D"></a>**[Decoupled-3D]** Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation [[AAAI2020](https://arxiv.org/abs/2002.01619)]
+
+## 2019
+- <a id="3d-vehicle-tracking"></a>**[3d-vehicle-tracking]** Joint Monocular 3D Vehicle Detection and Tracking [[ICCV2019](https://arxiv.org/pdf/1811.10742.pdf)][[Pytorch](https://github.com/ucbdrive/3d-vehicle-tracking)]
+- <a id="MonoDIS"></a>**[MonoDIS]** Disentangling monocular 3d object detection [[ICCV2019](https://openaccess.thecvf.com/content_ICCV_2019/papers/Simonelli_Disentangling_Monocular_3D_Object_Detection_ICCV_2019_paper.pdf)]
+- <a id="AM3D"></a>**[AM3D]** Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving [[ICCV2019](https://arxiv.org/abs/1903.11444)]
+- <a id="M3D-RPN"></a>**[M3D-RPN]** M3D-RPN: Monocular 3D Region Proposal Network for Object Detection [[ICCV2019(Oral)](https://arxiv.org/abs/1907.06038)][[Pytorch](https://github.com/garrickbrazil/M3D-RPN)]
+- <a id="MVRA"></a>**[MVRA]** Multi-View Reprojection Architecture for Orientation Estimation [[ICCVW2019](https://openaccess.thecvf.com/content_ICCVW_2019/papers/ADW/Choi_Multi-View_Reprojection_Architecture_for_Orientation_Estimation_ICCVW_2019_paper.pdf)]
+- <a id="Mono3DPLiDAR"></a>**[Mono3DPLiDAR]** Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [[ICCVW2019](https://arxiv.org/abs/1903.09847)]
+- <a id="MonoPSR"></a>**[MonoPSR]** Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [[CVPR2019](https://arxiv.org/abs/1904.01690)][[Pytorch](https://github.com/kujason/monopsr)]
+- <a id="FQNet"></a>**[FQNet]** Deep fitting degree scoring network for monocular 3d object detection [[CVPR2019](https://arxiv.org/abs/1904.12681)]
+- <a id="ROI-10D"></a>**[ROI-10D]** ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape [[CVPR2019](https://arxiv.org/abs/1812.02781)]
+- <a id="GS3D"></a>**[GS3D]** GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving [[CVPR2019](https://openaccess.thecvf.com/content_CVPR_2019/html/Li_GS3D_An_Efficient_3D_Object_Detection_Framework_for_Autonomous_Driving_CVPR_2019_paper.html)]
+- <a id="Pseudo-LiDAR"></a>**[Pseudo-LiDAR]** Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [[CVPR2019](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Pseudo-LiDAR_From_Visual_Depth_Estimation_Bridging_the_Gap_in_3D_CVPR_2019_paper.pdf)][[Pytorch](https://github.com/mileyan/pseudo_lidar)]
+- <a id="BirdGAN"></a>**[BirdGAN]** Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles [[IROS2019](https://arxiv.org/pdf/1904.08494.pdf)]
+- <a id="MonoGRNet"></a>**[MonoGRNet]** MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization [[AAAI2019(oral)](https://arxiv.org/abs/1811.10247)][[Tensorflow](https://github.com/Zengyi-Qin/MonoGRNet)]
+- <a id="OFT-Net"></a>**[OFT-Net]** Orthographic feature transform for monocular 3d object detection [[BMVC2019](https://bmvc2019.org/wp-content/uploads/papers/0328-paper.pdf)][[Pytorch](https://github.com/tom-roddick/oft)]
+- <a id="Shift R-CNN"></a>**[Shift R-CNN]** Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints [[TIP2019](https://arxiv.org/abs/1905.09970)]
+- <a id="SS3D"></a>**[SS3D]** SS3D: Monocular 3d object detection and box fitting trained end-to-end using intersection-over-union loss [[Arxiv2019](https://arxiv.org/abs/1906.08070)]
+
+## 2018
+- <a id="Multi-Fusion"></a>**[Multi-Fusion]** Multi-Level Fusion based 3D Object Detection from Monocular Images [[CVPR2018](https://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Multi-Level_Fusion_Based_CVPR_2018_paper.pdf)][[Pytorch](https://github.com/abbyxxn/maskrcnn-benchmark-3d)]
+- <a id="Mono3D++"></a>**[Mono3D++]** Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors [[AAAI2018](https://arxiv.org/abs/1901.03446)]
+
+## 2017
+- <a id="Deep3DBox"></a>**[Deep3DBox]** 3D Bounding Box Estimation Using Deep Learning and Geometry [[CVPR2017](https://arxiv.org/abs/1612.00496)][[Pytorch](https://github.com/skhadem/3D-BoundingBox)][[Tensorflow](https://github.com/smallcorgi/3D-Deepbox)]
+- <a id="Deep MANTA"></a>**[Deep MANTA]** Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image [[CVPR2017](https://arxiv.org/abs/1703.07570)]
+
+## 2016
+- <a id="Mono3D"></a>**[Mono3D]** Monocular 3D object detection for autonomous driving [[CVPR2016](https://www.cs.toronto.edu/~urtasun/publications/chen_etal_cvpr16.pdf)]
+
+
+
+# KITTI Results
+<!-- <font color=blue, size=4>val/test</font><font color=blue, size=3> (R<sub>11</sub>/R<sub>40</sub>) @ IOU=0.7</font> -->
+<!-- <style type="text/css">
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+  overflow:hidden;padding:5px 10px;word-break:normal;}
+.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
+  font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
+.tg .tg-9wq8{border-color:inherit;text-align:center;vertical-align:middle}
+.tg .tg-nrix{border-color:inherit;text-align:center;vertical-align:middle}
+</style> -->
+<table class="tg" style="text-align:center">
+<thead>
+  <tr>
+    <th class="tg-9wq8" rowspan="2">Method</th>
+    <th class="tg-nrix" rowspan="2">Extra</th>
+    <th class="tg-nrix" colspan="3">Test,      
+    AP<sub>3D</sub>|<sub>R<sub>40</sub></th>
+    <th class="tg-nrix" colspan="3">Val,      
+    AP<sub>3D</sub>|<sub>R<sub>40</sub></th>
+<!--       <th class="tg-nrix" colspan="3">Val, 
+    AP<sub>3D</sub>|<sub>R<sub>11</sub></th> -->
+    <th class="tg-nrix" rowspan="2">Reference</th>
+  </tr>
+  <tr>
+    <th class="tg-nrix">Easy</th>
+    <th class="tg-nrix">Mod.</th>
+    <th class="tg-nrix">Hard</th>
+    <th class="tg-nrix">Easy</th>
+    <th class="tg-nrix">Mod.</th>
+    <th class="tg-nrix">Hard</th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td class="tg-9wq8"><a href="#LPCG">LPCG</a></td>     
+    <td class="tg-nrix">Lidar+raw</td>  
+    <td class="tg-nrix">25.56</td>
+    <td class="tg-nrix">17.80</td>
+    <td class="tg-nrix">15.38</td>
+    <td class="tg-nrix">31.15</td>  
+    <td class="tg-nrix">23.42</td>
+    <td class="tg-nrix">20.60</td>
+    <td class="tg-nrix">ECCV2022</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#CMKD">CMKD</a></td>      
+    <td class="tg-nrix">Lidar+raw</td>  
+    <td class="tg-nrix">28.55</td>
+    <td class="tg-nrix">18.69</td>
+    <td class="tg-nrix">16.77</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">ECCV2022</td>
+  </tr>
+  <tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoPSR">MonoPSR</a></td>     
+    <td class="tg-nrix">Lidar</td>     
+    <td class="tg-nrix">10.76</td>
+    <td class="tg-nrix">7.25</td>
+    <td class="tg-nrix">5.85</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">CVPR2019</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoRUn">MonoRUn</a></td>     
+    <td class="tg-nrix">Lidar</td>     
+    <td class="tg-nrix">19.65</td>
+    <td class="tg-nrix">12.30</td>
+    <td class="tg-nrix">10.58</td>
+    <td class="tg-nrix">20.02</td>
+    <td class="tg-nrix">14.65</td>
+    <td class="tg-nrix">12.61</td>
+    <td class="tg-nrix">CVPR2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#CaDDN">CaDDN</a></td>     
+    <td class="tg-nrix">Lidar</td>     
+    <td class="tg-nrix">19.17</td>
+    <td class="tg-nrix">13.41</td>
+    <td class="tg-nrix">11.46</td>
+    <td class="tg-nrix">23.57</td>
+    <td class="tg-nrix">16.31</td>
+    <td class="tg-nrix">13.84</td>
+    <td class="tg-nrix">CVPR2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoDistill">MonoDistill</a></td>     
+    <td class="tg-nrix">Lidar</td>     
+    <td class="tg-nrix">22.97</td>
+    <td class="tg-nrix">16.03</td>
+    <td class="tg-nrix">13.60</td>
+    <td class="tg-nrix">24.31</td>
+    <td class="tg-nrix">18.47</td>
+    <td class="tg-nrix">15.76</td>
+    <td class="tg-nrix">ICLR2022</td>
+  </tr>
+  <tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#AM3D">AM3D</a></td>
+    <td class="tg-nrix">Depth</td>     
+    <td class="tg-nrix">16.50</td>
+    <td class="tg-nrix">10.74</td>
+    <td class="tg-nrix">9.52</td>
+    <td class="tg-nrix">28.31</td>
+    <td class="tg-nrix">15.76</td>
+    <td class="tg-nrix">12.24</td>
+    <td class="tg-nrix">ICCV2019</td>
+    </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#PatchNet">PatchNet</a></td>     
+    <td class="tg-nrix">Depth</td>     
+    <td class="tg-nrix">15.68</td>
+    <td class="tg-nrix">11.12</td>
+    <td class="tg-nrix">10.17</td>
+    <td class="tg-nrix">31.60</td>
+    <td class="tg-nrix">16.80</td>
+    <td class="tg-nrix">13.80</td>
+    <td class="tg-nrix">ECCV2020</td>
+  </tr>
+    <tr>
+    <td class="tg-9wq8"><a href="#D4LCN">D4LCN</a></td>     
+    <td class="tg-nrix">Depth</td>     
+    <td class="tg-nrix">16.65</td>
+    <td class="tg-nrix">11.72</td>
+    <td class="tg-nrix">9.51</td>
+    <td class="tg-nrix">22.32</td>
+    <td class="tg-nrix">16.20</td>
+    <td class="tg-nrix">12.30</td>
+    <td class="tg-nrix">CVPRW2020</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#DFR-Net">DFR-Net</a></td>     
+    <td class="tg-nrix">Depth</td>     
+    <td class="tg-nrix">19.40</td>
+    <td class="tg-nrix">13.63</td>
+    <td class="tg-nrix">10.35</td>
+    <td class="tg-nrix">24.81</td>
+    <td class="tg-nrix">17.78</td>
+    <td class="tg-nrix">14.41</td>
+    <td class="tg-nrix">ICCV2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#Pseudo-Stereo">Pseudo-Stereo</a></td>     
+    <td class="tg-nrix">Depth</td>     
+    <td class="tg-nrix">23.74</td>
+    <td class="tg-nrix">17.74</td>
+    <td class="tg-nrix">15.14</td>
+    <td class="tg-nrix">35.18</td>
+    <td class="tg-nrix">24.15</td>
+    <td class="tg-nrix">20.35</td>
+    <td class="tg-nrix">CVPR2022</td>
+  </tr>
+  <tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr>
+  <tr>
+    <td class="tg-9wq8" nowrap="nowrap"><a href="#M3D-RPN">M3D-RPN</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">14.76</td>
+    <td class="tg-nrix">9.71</td>
+    <td class="tg-nrix">7.42</td>
+    <td class="tg-nrix">14.53</td>
+    <td class="tg-nrix">11.07</td>
+    <td class="tg-nrix">8.65</td>
+    <td class="tg-nrix">ICCV2019</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#SMOKE">SMOKE</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">14.03</td>
+    <td class="tg-nrix">9.76</td>
+    <td class="tg-nrix">7.84 </td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">CVPRW2020</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoPair">MonoPair</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">13.04</td>
+    <td class="tg-nrix">9.99</td>
+    <td class="tg-nrix">8.65 </td>
+    <td class="tg-nrix">16.28</td>
+    <td class="tg-nrix">12.30</td>
+    <td class="tg-nrix">10.42</td>
+    <td class="tg-nrix">CVPR2020</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#RTM3D">RTM3D</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">14.41</td>
+    <td class="tg-nrix">10.34</td>
+    <td class="tg-nrix">8.77 </td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">ECCV2020</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#M3DSSD">M3DSSD</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">17.51</td>
+    <td class="tg-nrix">11.46</td>
+    <td class="tg-nrix">8.98 </td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">-</td>
+    <td class="tg-nrix">CVPR2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#Monoflex">Monoflex</a></td>
+    <td class="tg-nrix">None</td>
+    <td class="tg-nrix">19.94</td>
+    <td class="tg-nrix">13.89</td>
+    <td class="tg-nrix">12.07 </td>
+    <td class="tg-nrix">23.64</td>
+    <td class="tg-nrix">17.51</td>
+    <td class="tg-nrix">14.83</td>
+    <td class="tg-nrix">CVPR2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#GUPNet">GUPNet</a></td>     
+    <td class="tg-nrix">None</td>     
+    <td class="tg-nrix">20.11</td>
+    <td class="tg-nrix">14.20</td>
+    <td class="tg-nrix">11.77</td>
+    <td class="tg-nrix">22.76</td>
+    <td class="tg-nrix">16.46</td>
+    <td class="tg-nrix">13.72</td>
+    <td class="tg-nrix">ICCV2021</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoCon">MonoCon</a></td>     
+    <td class="tg-nrix">None</td>     
+    <td class="tg-nrix">22.50</td>
+    <td class="tg-nrix">16.46</td>
+    <td class="tg-nrix">13.95</td>
+    <td class="tg-nrix">26.33</td>
+    <td class="tg-nrix">19.01</td>
+    <td class="tg-nrix">15.98</td>
+    <td class="tg-nrix">AAAI2022</td>
+  </tr>
+  <tr>
+    <td class="tg-9wq8"><a href="#MonoDDE">MonoDDE</a></td>     
+    <td class="tg-nrix">None</td>     
+    <td class="tg-nrix">24.93</td>
+    <td class="tg-nrix">17.14</td>
+    <td class="tg-nrix">15.10</td>  
+    <td class="tg-nrix">26.66</td>
+    <td class="tg-nrix">19.75</td>
+    <td class="tg-nrix">16.72</td> 
+    <td class="tg-nrix">CVPR2022</td>
+  </tr>
+ <tr>
+    <td class="tg-9wq8"><a href="#MonoXiver">MonoXiver</a></td>     
+    <td class="tg-nrix">None</td>     
+    <td class="tg-nrix">25.24</td>
+    <td class="tg-nrix">19.04</td>
+    <td class="tg-nrix">16.39</td>  
+    <td class="tg-nrix">30.48</td>
+    <td class="tg-nrix">22.40</td>
+    <td class="tg-nrix">19.13</td> 
+    <td class="tg-nrix">ICCV2023</td>
+  </tr>
+      
+</tbody>
+</table>
+
+
+

-# Git 仓库初始化与推送指南 
- ## 1. Git账号、密码 
-   ```bash 
-  Git 账号和密码信息请在“工程实践项目管理” - “我的项目”模块中的“我的信息”处查看。 
-  ``` 
- ## 2. 创建新的 Git 仓库并推送 
- ```bash 
- # 创建项目目录并进入 
- mkdir Monocular3DDetection 
- cd Monocular3DDetection 
- # 初始化本地 Git 仓库 
- git init 
- # 创建一个 info 文件（可选） 
- touch info.md 
- # 将文件添加到暂存区 
- git add info.md 
- # 提交到本地仓库 
- git commit -m 'first commit' 
- # 创建本地分支admin 
- git branch admin 
- # 设置admin为当前分支 
- git checkout admin 
- # 添加远程仓库地址（别名为 origin） 
- git remote add origin http://college.ithyxy.com:21147/inter/Monocular3DDetection.git 
- # 先拉取远程admin分支的内容，并合并到本地 
- git pull origin admin --allow-unrelated-histories 
- # 推送到远程仓库的 admin 分支，并设置上游跟踪 
- git push -u origin 'admin' 
- ``` 
- ## 3. 已有仓库? 
- ```bash 
- cd existing_git_repo 
- git remote add origin http://college.ithyxy.com:21147/inter/Monocular3DDetection.git 
- git push -u origin 'admin' 
- ```