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awesome-Automanous-3D-detection-methods

3D目标检测最新论文与code
https://github.com/qxiaofan/awesome-Automanous-3D-detection-methods

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  • 2019

    • keywords

      • [CVPR
      • [CVPR - LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. [[code](https://github.com/mileyan/pseudo_lidar)] [__`stereo`__][__`kitti`__]
      • [CVPR
      • [CVPR
      • [CVPR
      • [ICCV - CNN. [__`lidar`__] [__`kitti`__]
      • [ICCV - to-Dense 3D Object Detector for Point Cloud.[[pytorch](https://github.com/tomztyang/3DSSD)] [__`lidar`__] [__`kitti`__]
      • [ICCVW
      • [NeurIPS - Voxel CNN for Efficient 3D Deep Learning. [__`lidar`__] [__`kitti`__]
      • [ICMLW
      • [ICCV - RPN: Monocular 3D Region Proposal Network for Object Detection.[[pytorch](http://cvlab.cse.msu.edu/project-m3d-rpn.html)] [__`monocular`__] [__`kitti`__]
      • [ICRA - ram/FL3D)] [__`lidar`__] [__`kitti`__]
      • [ICRA - VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data. [__`lidar`__] [__`kitti`__]
      • [ICRA - Net: Multimodal VoxelNet for 3D Object Detection. [__`lidar`__] [__`kitti`__]
      • [IROS - Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [[tensorflow](https://github.com/Merium88/Edge-Aware-PointNet)] [__`lidar`__] [__`kitti`__]
      • [arXiv - LiDAR Point Cloud. [__`monocular`__][__`kitti`__]
      • [arXiv - View Proposal Generation for Real-Time Object Detection from Point Clouds. [[code](https://github.com/LordLiang/FVNet)] [__`lidar`__] [__`kitti`__]
      • [CVPRW - YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)] [__`monocular`__][__`kitti`__]:fire:
      • [CVPR
      • [CVPR
      • [CVPR - 10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. [__`monocular`__][__`kitti`__]
      • [CoRR
      • [arXiv
      • [arXiv
      • [IROS - Wise Features for Amodal 3D Object Detection. [[pytorch](https://github.com/zhixinwang/frustum-convnet)] [__`lidar+image`__] [__`kitti`__]
      • [IROS - view synthesis orientation estimation. [__`lidar`__] [__`kitti`__]
      • [3DV
      • [CVPR - CNN based 3D Object Detection for Autonomous Driving. [[code](https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN)] [__`stereo`__][__`kitti`__]
      • [AAAI
      • [CVPR
      • [CVPR - Qin/TLNet)] [__`stereo`__][__`kitti`__]
      • [arXiv
      • [NeurIPS - Voxel CNN for Efficient 3D Deep Learning. [__`lidar`__] [__`kitti`__]

  • 2020

    • keywords

      • [ECCV - Voxel Convolution.[__`lidar`__]
      • [ACMMM
      • [arxiv
      • [arxiv - Modality 3D Object Detection [__`lidar`__][__`kitti`__]
      • [arxiv - RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges[__`lidar`__][__`kitti`__][__`waymo`__]
      • [arxiv - Aware Data Augmentation for 3D Object Detection in Point Cloud. [__`lidar`__][__`kitti`__]
      • [arxiv - Aware PointRCNN for 3D Objection Detection in Point Clouds. [__`lidar`__][__`kitti`__]
      • [arxiv - Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles
      • [arxiv
      • [arxiv - LiDAR 3D Object Detection for Autonomous Driving. [__`lidar`__]
      • [arxiv - Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. [__`lidar`__][__`kitti`__]
      • [arxiv
      • [arxiv
      • [arxiv - Frame to Single-Frame: Knowledge Distillation for 3D Object Detection. [__`lidar`__][__`waymo`__]
      • [arxiv - Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. [__`lidar`__][__`kitti`__]
      • [CVPR
      • [CVPR - RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. [[pytorch](https://github.com/open-mmlab/OpenPCDet)] [__`lidar`__] [__`kitti`__] [__`waymo`__]:fire: :star: :fire: :star:
      • [CVPR - based 3D Single Stage Object Detector. [[TensorFlow](https://github.com/tomztyang/3DSSD)] [__`lidar`__] [__`kitti`__][__`nusc`__] :fire: :star:
      • [CVPR
      • [CVPR - Guided Convolutions for Monocular 3D Object Detection.[[code](https://github.com/dingmyu/D4LCN)] [__`monocular`__] [__`kitti`__]
      • [ECCV - based Object Detection for Autonomous Driving.[__`lidar`__] [__`waymo`__]
      • [ECCV
      • [arxiv - based 3D Object Detection and Tracking [[code](https://github.com/tianweiy/CenterPoint)][__`lidar`__][__`nusc`__]
      • [arxiv
      • [arxiv - Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
      • [arxiv
      • [arxiv - Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss. [__`lidar`__][__`kitti`__]
      • [arxiv - Guided Cylindrical Network for 3D Object Detection. [__`lidar`__][__`kitti`__]
      • [ECCV - perception-light-curtains)][__`lidar`__]
      • [ECCV - Voxel Convolution.[__`lidar`__]
      • [ACMMM
      • [arxiv
      • [arxiv - class Object Detection from Point Clouds [[code](https://github.com/xinge008/SSN)][__`lidar`__][__`kitti`__]
      • [arxiv
      • [arxiv - Modality 3D Object Detection [__`lidar`__][__`kitti`__]
      • [arxiv - Modal 360∘ Perception Proposal[__`lidar`__][__`kitti`__]
      • [arxiv - LiDAR Object Candidates Fusion for 3D Object Detection[__`lidar+image`__][__`kitti`__]
      • [arxiv
      • [arxiv - RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges[__`lidar`__][__`kitti`__][__`waymo`__]
      • [arxiv - Aware Data Augmentation for 3D Object Detection in Point Cloud. [__`lidar`__][__`kitti`__]
      • [arxiv - 3D Detection and Domain Adaptation. [__`lidar`__][__`waymo`__]
      • [arxiv
      • [arxiv - Aware PointRCNN for 3D Objection Detection in Point Clouds. [__`lidar`__][__`kitti`__]
      • [arxiv - Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles
      • [arxiv
      • [arxiv - Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss. [__`lidar`__][__`kitti`__]['det_and_tracking']
      • [TPAMI - A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. [[pytorch](https://github.com/open-mmlab/OpenPCDet)][__`lidar`__] [__`kitti`__]
      • [AAAI
      • [AAAI - Guided Depth Estimation. [__`monocular`__] [__`kitti`__]
      • [CVPR - 3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. [__`lidar`__] [__`kitti`__]
      • [CVPR - CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation.[[code](https://github.com/zju3dv/disprcnn)] [__`stereo`__] [__`kitti`__]
      • [CVPR
      • [CVPR - Centric Metrics. [__`lidar`__]
      • [CVPR
      • [ECCV
      • [ECCV - LiDAR Representation.[[code](https://github.com/xinzhuma/patchnet)][__`monocular`__] [__`kitti`__]
      • [ECCV
      • [arxiv - Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. [__`lidar`__][__`kitti`__]
      • [arxiv - View Adaptive Fusion Network for 3D Object Detection. [__`lidar`__][__`kitti`__]
      • [arxiv
      • [arxiv - LiDAR 3D Object Detection for Autonomous Driving. [__`lidar`__]
      • [arxiv
      • [arxiv
      • [arxiv - Frame to Single-Frame: Knowledge Distillation for 3D Object Detection. [__`lidar`__][__`waymo`__]
      • [arxiv - Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. [__`lidar`__][__`kitti`__]
      • [arxiv
      • [arxiv - Aware Hardening of 3D Object Detection Neural Network Systems [__`lidar`__][__`kitti`__]
      • [CVPR
      • [CVPR
      • [CVPR - Centric Metrics. [__`lidar`__]
      • [ECCV - CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection.[__`lidar+image`__] [__`kitti`__]
      • [ECCV - kinematic.html)][__`monocular_video`__] [__`kitti`__]
      • [ECCV
      • [arxiv - time 3D object proposal generation and classification under limited processing resources [__`lidar`__][__`kitti`__]
      • [arxiv
      • [AAAI - Guided Depth Estimation. [__`monocular`__] [__`kitti`__]
      • [CVPR - CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation.[[code](https://github.com/zju3dv/disprcnn)] [__`stereo`__] [__`kitti`__]
      • [ECCV - LiDAR Representation.[[code](https://github.com/xinzhuma/patchnet)][__`monocular`__] [__`kitti`__]
      • [arxiv
      • [arxiv
      • [arxiv - class Object Detection from Point Clouds [[code](https://github.com/xinge008/SSN)][__`lidar`__][__`kitti`__]
      • [arxiv
      • [CVPR
      • [ECCV
      • [arxiv - based Radar and Camera Fusion for 3D Object Detection. [__`radar+image`__][__`nusc`__]
      • [AAAI - RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. [__`lidar+image`__] [__`kitti`__]
      • [AAAI - Aware Adaptive Zooming Neural Network for 3D Object Detection. [[code](https://github.com/detectRecog/ZoomNet)] [__`stereo`__] [__`kitti`__]
      • [CVPR - GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [[TensorFlow](https://github.com/WeijingShi/Point-GNN)] [__`lidar`__] [__`kitti`__] :fire: :star:
      • [CVPR - based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [__`lidar_video`__] [__`nusc`__]
      • [CVPR
      • [ECCVW - RCNN: Improving 3D Object Detection with Learned Deformations.[[code](https://github.com/AutoVision-cloud/Deformable-PV-RCNN)][__`lidar`__] [__`kitti`__]
      • [IROS - Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views.[__`lidar`__] [__`nusc`__]
      • [BMVC - FuseNet: Range View based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting [__`lidar`__][__`nusc`__]
      • [arxiv - Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds [__`lidar`__][__`nusc`__]
      • [arxiv
      • [arxiv
      • [arxiv - SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. [__`lidar`__][__`kitti`__]
      • [arxiv
      • [arxiv - Net: IoU Guided 3D Object Detector for Point Clouds [__`lidar`__][__`kitti`__]
      • [AAAI

  • Suvery

  • 2017

  • 2018

    • keywords

      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [ICRA
      • [IROS
      • [IROS
      • [SENSORS
      • [arXiv - based Object Detector for Point Cloud. [__`image+lidar`__] [__`kitti`__]
      • [arXiv - YOLO: Real-time 3D Object Detection on Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)] [__`lidar`__] [__`kitti`__] :fire:
      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [IROS
      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [IROS
      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [IROS
      • [ICRA - to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__]
      • [ICRA - Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]
      • [IROS

  • note

  • code base

Programming Languages
Python 3
Categories
2020 100 2019 33 2018 22 Suvery 6 code base 3 2017 2 note 1
Sub Categories
keywords 166
Keywords
object-detection 3 pytorch 2 point-cloud 2 3d-object-detection 1 pv-rcnn 1 autonomous-driving 1 3d-detection 1 voxelnet 1 nuscenes 1 kitti 1