diff --git a/README.md b/README.md index efb2767..0a00fcc 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,410 @@ -# 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 +- **[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)] +- **[MonoTTA]** Fully Test-Time Adaptation for Monocular 3D Object Detection [[ECCV2024](https://arxiv.org/pdf/2405.19682)][[Pytorch](https://github.com/Hongbin98/MonoTTA)] +- **[MonoMAE]** MonoMAE: Enhancing Monocular 3D Detection through Depth-Aware Masked Autoencoders [[NeurIPS2024](https://arxiv.org/pdf/2405.07696)] +- **[OVM3D]** Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data [[NeurIPS2024](https://arxiv.org/pdf/2411.15657)] +- **[MonoCD]** MonoCD: Monocular 3D Object Detection with Complementary Depths [[CVPR2024](https://arxiv.org/pdf/2404.03181)][[Pytorch](https://github.com/elvintanhust/MonoCD)] +- **[DPL]** Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2403.17387)] +- **[UniMODE]** UniMODE: Unified Monocular 3D Object Detection [[CVPR2024](https://arxiv.org/pdf/2402.18573)] +- **[YOLOBU]** You Only Look Bottom-Up for Monocular 3D Object Detection [[RA-L2024](https://arxiv.org/pdf/2401.15319)] + +## 2023 +- **[DDML]** Depth-discriminative Metric Learning for Monocular 3D Object Detection [[NeurIPS2023](https://arxiv.org/pdf/2401.01075.pdf)] +- **[MonoXiver]** Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver [[ICCV2023](https://arxiv.org/pdf/2304.01289.pdf)] +- **[MonoNeRD]** MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection [[ICCV2023](https://arxiv.org/pdf/2308.09421.pdf)][[Pytorch](https://github.com/cskkxjk/MonoNeRD)] +- **[MonoATT]** MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer [[CVPR2023](https://arxiv.org/abs/2303.13018)] +- **[WeakMono3D]** Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency [[CVPR2023](https://arxiv.org/pdf/2303.08686.pdf)] +- **[MonoPGC]** MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts [[ICRA2023](https://arxiv.org/pdf/2302.10549.pdf)] +- **[ADD]** Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection[[AAAI2023](https://arxiv.org/pdf/2211.16779.pdf)] + +## 2022 +- **[MoGDE]** MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation [[NeurIPS2022](https://arxiv.org/abs/2303.13561)] +- **[LPCG]** Lidar Point Cloud Guided Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/abs/2104.09035)][[Pytorch](https://github.com/SPengLiang/LPCG)] +- **[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)] +- **[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)] +- **[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)] +- **[DEVIANT]** DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/pdf/2207.10758.pdf)][[Pytorch](https://github.com/abhi1kumar/DEVIANT)] +- **[DCD]** Densely Constrained Depth Estimator for Monocular 3D Object Detection [[ECCV2022](https://arxiv.org/pdf/2207.10047.pdf)][[Pytorch](https://github.com/BraveGroup/DCD)] +- **[STMono3D]** Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [[ECCV2022](https://arxiv.org/pdf/2204.11590.pdf)] +- **[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)] +- **[SGM3D]** SGM3D: Stereo Guided Monocular 3D Object Detection [[RA-L2022](https://arxiv.org/pdf/2112.01914.pdf)][[Pytorch](https://github.com/zhouzheyuan/sgm3d)] +- **[PRT]** Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking [[ICRA2022](https://arxiv.org/pdf/2206.03666.pdf)] +- **[Time3D]** Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving [[CVPR2022](https://arxiv.org/pdf/2205.14882.pdf)] +- **[MonoGround]** MonoGround: Detecting Monocular 3D Objects from the Ground [[CVPR2022](https://arxiv.org/pdf/2206.07372.pdf)][[Pytorch](https://github.com/cfzd/MonoGround)] +- **[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)] +- **[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)] +- **[MonoDDE]** Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection [[CVPR2022](https://arxiv.org/pdf/2205.09373.pdf)] +- **[Homography]** Homography Loss for Monocular 3D Object Detection [[CVPR2022](https://arxiv.org/pdf/2204.00754.pdf)] +- **[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)] +- **[MonoDTR]** MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer [[CVPR2022](https://arxiv.org/pdf/2203.10981.pdf)][[Pytorch](https://github.com/kuanchihhuang/MonoDTR)] +- **[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)] +- **[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)] +- **[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)] +- **[WeakM3D]** WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection [[ICLR2022](https://openreview.net/pdf?id=ahi2XSHpAUZ)][[Pytorch](https://github.com/SPengLiang/WeakM3D)] +- **[MonoCon]** Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection [[AAAI2022](https://arxiv.org/pdf/2112.04628.pdf)][[Pytorch](https://github.com/Xianpeng919/MonoCon)] +- **[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 +- **[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)] +- **[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)] +- **[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)] +- **[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)] +- **[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)] +- **[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)] +- **[MonoRCNN]** Geometry-based Distance Decomposition for Monocular 3D Object Detection [[ICCV2021](https://arxiv.org/abs/2104.03775)][[Pytorch](https://github.com/Rock-100/MonoDet)] +- **[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)] +- **[GUPNet]** Geometry Uncertainty Projection Network for Monocular 3D Object Detection [[ICCV2021](https://arxiv.org/pdf/2107.13774.pdf)][[Pytorch](https://github.com/SuperMHP/GUPNet)] +- **[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)] +- **[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)] +- **[monodle]** Delving into Localization Errors for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.16237)][[Pytorch](https://github.com/xinzhuma/monodle)] +- **[Monoflex]** Objects are Different: Flexible Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2104.02323)][[Pytorch](https://github.com/zhangyp15/MonoFlex)] +- **[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)] +- **[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)] +- **[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)] +- **[M3DSSD]** M3DSSD: Monocular 3D Single Stage Object Detector [[CVPR2021](https://arxiv.org/abs/2103.13164)][[Pytorch](https://github.com/mumianyuxin/M3DSSD)] +- **[CaDDN]** Categorical Depth Distribution Network for Monocular 3D Object Detection [[CVPR2021](https://arxiv.org/abs/2103.01100)][[Pytorch](https://github.com/TRAILab/CaDDN)] +- **[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 +- **[UR3D]** Distance-Normalized Unified Representation for Monocular 3D Object Detection [[ECCV2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740086.pdf)] +- **[MonoDR]** Monocular Differentiable Rendering for Self-Supervised 3D Object Detection [[ECCV2020](https://arxiv.org/abs/2009.14524)] +- **[DA-3Ddet]** Monocular 3d object detection via feature domain adaptation [[ECCV2020](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540018.pdf)] +- **[MoVi-3D]** Towards generalization across depth for monocular 3d object detection [[ECCV2020](https://arxiv.org/abs/1912.08035)] +- **[PatchNet]** Rethinking Pseudo-LiDAR Representation [[ECCV2020](https://arxiv.org/abs/2008.04582)][[Pytorch](https://github.com/xinzhuma/patchnet)] +- **[RAR-Net]** Reinforced Axial Refinement Network for Monocular 3D Object Detection [[ECCV2020](https://arxiv.org/abs/2008.13748)] +- **[kinematic3d]** Kinematic 3D Object Detection in Monocular Video [[ECCV2020](https://arxiv.org/abs/2007.09548)][[Pytorch](https://github.com/garrickbrazil/kinematic3d)] +- **[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)] +- **[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)] +- **[D4LCN]** Learning Depth-Guided Convolutions for Monocular 3D Object Detection [[CVPRW2020](https://arxiv.org/abs/1912.04799)][[Pytorch](https://github.com/dingmyu/D4LCN)] +- **[MonoPair]** MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships [[CVPR2020](https://arxiv.org/abs/2003.00504)] +- **[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)] +- **[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)] +- **[OACV]** Object-Aware Centroid Voting for Monocular 3D Object Detection [[IROS2020](https://arxiv.org/abs/2007.09836)] +- **[MonoGRNet_v2]** Monocular 3D Object Detection via Geometric Reasoning on Keypoints [[VISIGRAPP2020](https://arxiv.org/abs/1905.05618)] +- **[ForeSeE]** Task-Aware Monocular Depth Estimation for 3D Object Detection [[AAAI2020(oral)](https://arxiv.org/abs/1909.07701)][[Pytorch](https://github.com/WXinlong/ForeSeE)] +- **[Decoupled-3D]** Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation [[AAAI2020](https://arxiv.org/abs/2002.01619)] + +## 2019 +- **[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)] +- **[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)] +- **[AM3D]** Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving [[ICCV2019](https://arxiv.org/abs/1903.11444)] +- **[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)] +- **[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)] +- **[Mono3DPLiDAR]** Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [[ICCVW2019](https://arxiv.org/abs/1903.09847)] +- **[MonoPSR]** Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [[CVPR2019](https://arxiv.org/abs/1904.01690)][[Pytorch](https://github.com/kujason/monopsr)] +- **[FQNet]** Deep fitting degree scoring network for monocular 3d object detection [[CVPR2019](https://arxiv.org/abs/1904.12681)] +- **[ROI-10D]** ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape [[CVPR2019](https://arxiv.org/abs/1812.02781)] +- **[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)] +- **[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)] +- **[BirdGAN]** Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles [[IROS2019](https://arxiv.org/pdf/1904.08494.pdf)] +- **[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)] +- **[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)] +- **[Shift R-CNN]** Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints [[TIP2019](https://arxiv.org/abs/1905.09970)] +- **[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 +- **[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)] +- **[Mono3D++]** Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors [[AAAI2018](https://arxiv.org/abs/1901.03446)] + +## 2017 +- **[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)] +- **[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 +- **[Mono3D]** Monocular 3D object detection for autonomous driving [[CVPR2016](https://www.cs.toronto.edu/~urtasun/publications/chen_etal_cvpr16.pdf)] + + + +# KITTI Results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MethodExtraTest, + AP3D|R40Val, + AP3D|R40Reference
EasyMod.HardEasyMod.Hard
LPCGLidar+raw25.5617.8015.3831.1523.4220.60ECCV2022
CMKDLidar+raw28.5518.6916.77---ECCV2022
MonoPSRLidar10.767.255.85---CVPR2019
MonoRUnLidar19.6512.3010.5820.0214.6512.61CVPR2021
CaDDNLidar19.1713.4111.4623.5716.3113.84CVPR2021
MonoDistillLidar22.9716.0313.6024.3118.4715.76ICLR2022
AM3DDepth16.5010.749.5228.3115.7612.24ICCV2019
PatchNetDepth15.6811.1210.1731.6016.8013.80ECCV2020
D4LCNDepth16.6511.729.5122.3216.2012.30CVPRW2020
DFR-NetDepth19.4013.6310.3524.8117.7814.41ICCV2021
Pseudo-StereoDepth23.7417.7415.1435.1824.1520.35CVPR2022
M3D-RPNNone14.769.717.4214.5311.078.65ICCV2019
SMOKENone14.039.767.84 ---CVPRW2020
MonoPairNone13.049.998.65 16.2812.3010.42CVPR2020
RTM3DNone14.4110.348.77 ---ECCV2020
M3DSSDNone17.5111.468.98 ---CVPR2021
MonoflexNone19.9413.8912.07 23.6417.5114.83CVPR2021
GUPNetNone20.1114.2011.7722.7616.4613.72ICCV2021
MonoConNone22.5016.4613.9526.3319.0115.98AAAI2022
MonoDDENone24.9317.1415.1026.6619.7516.72CVPR2022
MonoXiverNone25.2419.0416.3930.4822.4019.13ICCV2023
+ + + -# 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' - ``` \ No newline at end of file