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https://github.com/tyjiang1997/awesome-Automanous-3D-detection-methods


https://github.com/tyjiang1997/awesome-Automanous-3D-detection-methods

List: awesome-Automanous-3D-detection-methods

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# Awesome-Aut-3D-Detection-Methods 

## note

This repository is created to collect excellent works on 3D object detection for autonomous driving tasks. We will update the latest papers as soon as possible. 

### keywords

#### inputs

__`monocular`__: monocular   __`stereo`__: stereo  __`lidar`__: point cloud  

__`image+lidar`__: image+lidar fusion  

#### datasets

experiments on datasets： __`kitti`__: KITTI   __`nuse`__: NuScenes   __`waymo`__: Waymo   __`ATG4D`__: ATG4D   __`lyft`__: lyft  

#### code

framework ： __`Tensorflow`__: TensorFlow   __`PyTorch`__: PyTorch  



## 2017 

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf)] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. [[tensorflow](https://github.com/charlesq34/pointnet)][[pytorch](https://github.com/fxia22/pointnet.pytorch)] [__`lidar`__] :fire::star:

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Multi-View_3D_Object_CVPR_2017_paper.pdf)] Multi-View 3D Object Detection Network for Autonomous Driving. [[tensorflow](https://github.com/bostondiditeam/MV3D)] [__`image+lidar`__] [__`kitti`__]:fire: :star:

 - [[ICRA](https://ieeexplore.ieee.org/document/7989161)] Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. [[code_matlab](https://github.com/lijiannuist/Vote3Deep_lidar)] [__`lidar`__] [__`kitti`__]:star:

 - [[IROS](https://ieeexplore.ieee.org/document/8205955)] 3D fully convolutional network for vehicle detection in point cloud. [[tensorflow](https://github.com/yukitsuji/3D_CNN_tensorflow)] [__`lidar`__] [__`kitti`__]:fire: :star:

## 2018

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.pdf)] PIXOR: Real-time 3D Object Detection from Point Clouds. [[pytorch](https://github.com/ankita-kalra/PIXOR)]  [__`lidar`__] [__`kitti`__][__`ATG4D`__]

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_VoxelNet_End-to-End_Learning_CVPR_2018_paper.pdf)] VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. [[tensorflow](https://github.com/tsinghua-rll/VoxelNet-tensorflow)] [__`lidar`__] [__`kitti`__]:fire::fire::fire: :star:

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_PointFusion_Deep_Sensor_CVPR_2018_paper.pdf)] PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation. [[code](https://github.com/malavikabindhi/CS230-PointFusion)] [__`image+lidar`__] [__`kitti`__]

 - [[CVPR](http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf)] Frustum PointNets for 3D Object Detection from RGB-D Data. [[tensorflow](https://github.com/charlesq34/frustum-pointnets)] [__`image+lidar`__] [__`kitti`__] :fire: :star:

 - [[ECCV](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf)] Deep Continuous Fusion for Multi-Sensor 3D Object Detection. [__`image+lidar`__] [__`kitti`__] [__`ATG4D`__]

 - [[ECCVW](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11131/Ali_YOLO3D_End-to-end_real-time_3D_Oriented_Object_Bounding_Box_Detection_from_ECCVW_2018_paper.pdf)] YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud. [ __`monocular`__] [__`kitti`__]

 - [[ICRA](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8462884)] End-to-end Learning of Multi-sensor 3D Tracking by Detection. [__`image+lidar`__] [__`kitti`__] 

 - [[ICRA](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8461257)] Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications. [__`lidar`__] [__`kitti`__]

 - [[ICRA](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8461232)] A General Pipeline for 3D Detection of Vehicles.[__`lidar`__] [__`kitti`__]

 - [[IROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8594362)] Joint 3D Proposal Generation and Object Detection from View Aggregation. [__`lidar`__] [__`kitti`__]:star:

 - [[IROS](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8593910)] Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding. [__`lidar`__] [__`kitti`__]

 - [[SENSORS](https://www.mdpi.com/1424-8220/18/10/3337)] SECOND: Sparsely Embedded Convolutional Detection. [[pytorch](https://github.com/traveller59/second.pytorch)][__`lidar`__] [__`kitti`__] :fire::fire::fire::fire:

 - [[arXiv](https://arxiv.org/abs/1812.05276)] IPOD: Intensive Point-based Object Detector for Point Cloud. [__`image+lidar`__] [__`kitti`__]

 - [[arXiv](https://arxiv.org/abs/1803.06199)] Complex-YOLO: Real-time 3D Object Detection on Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)]  [__`lidar`__] [__`kitti`__] :fire:

## 2019

 - [[CVPR](https://arxiv.org/abs/1812.07179)] Pseudo-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](https://arxiv.org/pdf/1902.09738)]  Stereo R-CNN based 3D Object Detection for Autonomous Driving. [[code](https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN)] [__`stereo`__][__`kitti`__]

 - [[CVPR](https://arxiv.org/abs/1812.04244)] PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. [[pytorch](https://github.com/sshaoshuai/PointRCNN)] [__`lidar`__] [__`kitti`__]:fire:

 - [[CVPR](https://arxiv.org/abs/1812.05784)] PointPillars: Fast Encoders for Object Detection from Point Clouds. [[pytorch](https://github.com/nutonomy/second.pytorch)] [__`lidar`__] [__`kitti`__]:fire:

 - [[CVPR](https://arxiv.org/abs/1903.08701v1)] LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving.[__`lidar`__] [__`kitti`__][__`ATG4D`__]

 - [[CVPRW](http://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Paigwar_Attentional_PointNet_for_3D-Object_Detection_in_Point_Clouds_CVPRW_2019_paper.pdf)] Attentional PointNet for 3D-Object Detection in Point Clouds. [[pytorch](https://github.com/anshulpaigwar/Attentional-PointNet)] [__`lidar`__] [__`kitti`__]

 - [[ICCV](https://arxiv.org/abs/1908.02990)] Fast Point R-CNN. [__`lidar`__] [__`kitti`__]

 - [[ICCV](https://arxiv.org/pdf/1907.10471)] STD: Sparse-to-Dense 3D Object Detector for Point Cloud.[[pytorch](https://github.com/tomztyang/3DSSD)] [__`lidar`__] [__`kitti`__]

 - [[ICCV](https://arxiv.org/pdf/1907.06038)] M3D-RPN: Monocular 3D Region Proposal Network for Object Detection.[[pytorch](http://cvlab.cse.msu.edu/project-m3d-rpn.html)] [__`monocular`__] [__`kitti`__]

 - [[ICCVW](https://arxiv.org/abs/1909.12249)] Range Adaptation for 3D Object Detection in LiDAR. [__`lidar`__] [__`kitti`__]

 - [[ICCVW](http://scholar.google.de/scholar?q=Multi-View%20Reprojection%20Architecture%20for%20Orientation%20Estimation)] Multi-View Reprojection Architecture for Orientation Estimation. [__`monocular`__] [__`kitti`__]

- [[NeurIPS](https://arxiv.org/pdf/1907.03739.pdf)] Point-Voxel CNN for Efficient 3D Deep Learning. [__`lidar`__] [__`kitti`__]

- [[ICMLW](https://arxiv.org/abs/1905.07290)] LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving. [__`lidar`__] 

- [[ICRA](https://arxiv.org/abs/1809.06065)] Focal Loss in 3D Object Detection. [[code](https://github.com/pyun-ram/FL3D)] [__`lidar`__] [__`kitti`__]

- [[ICRA](https://ras.papercept.net/conferences/conferences/ICRA19/program/ICRA19_ContentListWeb_2.html)] SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data. [__`lidar`__] [__`kitti`__]

- [[ICRA](https://arxiv.org/abs/1904.01649)] MVX-Net: Multimodal VoxelNet for 3D Object Detection. [__`lidar`__] [__`kitti`__]

- [[AAAI](https://arxiv.org/pdf/1811.10247)] MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. [__`monocular`__] [__`kitti`__]

- [[IROS](https://www.researchgate.net/publication/334720713_EPN_Edge-Aware_PointNet_for_Object_Recognition_from_Multi-View_25D_Point_Clouds)] EPN: Edge-Aware PointNet for Object Recognition from Multi-View 2.5D Point Clouds. [[tensorflow](https://github.com/Merium88/Edge-Aware-PointNet)] [__`lidar`__] [__`kitti`__]

- [[IROS](https://arxiv.org/pdf/1903.01864)] Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. [[pytorch](https://github.com/zhixinwang/frustum-convnet)] [__`lidar+image`__] [__`kitti`__]

- [[IROS](https://arxiv.org/pdf/1907.06777)] Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. [__`lidar`__] [__`kitti`__]

- [[3DV](https://arxiv.org/pdf/1908.03851)] IoU Loss for 2D/3D Object Detection. [__`lidar`__] [__`kitti`__]

- [[arXiv](https://arxiv.org/abs/1903.09847)] Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. [__`monocular`__][__`kitti`__]

- [[arXiv](https://arxiv.org/abs/1903.10750)] FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds. [[code](https://github.com/LordLiang/FVNet)] [__`lidar`__] [__`kitti`__]

- [[CVPRW](https://arxiv.org/abs/1904.07537)] Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. [[pytorch](https://github.com/AI-liu/Complex-YOLO)] [__`monocular`__][__`kitti`__]:fire:

- [[CVPR](https://arxiv.org/pdf/1904.01690)] Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. [[pytorch](https://github.com/kujason/monopsr)] [__`monocular`__][__`kitti`__]

- [[CVPR](https://arxiv.org/pdf/1903.10955.pdf)] GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving.  [__`monocular`__][__`kitti`__]

- [[CVPR](https://arxiv.org/pdf/1812.02781.pdf)]  ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape.  [__`monocular`__][__`kitti`__]

- [[CVPR](https://arxiv.org/pdf/1906.01193)] Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. [[pytorch](https://github.com/Zengyi-Qin/TLNet)] [__`stereo`__][__`kitti`__]

- [[CoRR](https://arxiv.org/abs/1901.08373)] 3D Backbone Network for 3D Object Detection. [[code](https://github.com/Benzlxs/tDBN)] [__`lidar`__] [__`kitti`__]

- [[arXiv](https://arxiv.org/abs/1903.11027)] nuScenes: A multimodal dataset for autonomous driving. [[link](https://www.nuscenes.org/overview)] [__`dataset`__]

- [[arXiv](https://arxiv.org/pdf/1907.13079.pdf)] Deformable Filter Convolution for Point Cloud Reasoning.[__`lidar`__] [__`kitti`__][__`ATG4D`__]

- [[arXiv](https://arxiv.org/pdf/1911.12236)] PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement.[__`lidar`__] [__`kitti`__][__`ATG4D`__]

## 2020



 - [[TPAMI](https://arxiv.org/abs/1907.03670)] Part-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](https://arxiv.org/pdf/1912.05163.pdf)] TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. [[code](https://github.com/happinesslz/TANet)] [__`lidar`__] [__`kitti`__]

- [[AAAI](https://arxiv.org/pdf/1911.06084)] PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module.  [__`lidar+image`__] [__`kitti`__]

- [[AAAI](https://arxiv.org/pdf/2003.00529)] ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection.  [[code](https://github.com/detectRecog/ZoomNet)] [__`stereo`__] [__`kitti`__]

- [[AAAI](https://arxiv.org/pdf/2002.01619)] Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation.  [__`monocular`__] [__`kitti`__]

- [[CVPR](https://arxiv.org/pdf/1912.13192)] PV-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](http://scholar.google.de/scholar?q=Structure%20Aware%20Single-stage%203D%20Object%20Detection%20from%20Point%20Cloud)] Structure Aware Single-stage 3D Object Detection from Point Cloud. [[pytorch](https://github.com/skyhehe123/SA-SSD)] [__`lidar`__] [__`kitti`__] :fire: :star: 

- [[CVPR](https://arxiv.org/pdf/2002.10187)]3DSSD: Point-based 3D Single Stage Object Detector. [[TensorFlow](https://github.com/tomztyang/3DSSD)] [__`lidar`__] [__`kitti`__][__`nusc`__] :fire: :star: 

- [[CVPR](https://arxiv.org/pdf/2003.01251)]Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. [[TensorFlow](https://github.com/WeijingShi/Point-GNN)] [__`lidar`__] [__`kitti`__] :fire: :star: 

- [[CVPR](https://arxiv.org/pdf/2006.04356)]Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection.  [__`lidar`__] [__`kitti`__] 

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2020/papers/Liang_PnPNet_End-to-End_Perception_and_Prediction_With_Tracking_in_the_Loop_CVPR_2020_paper.pdf)]PnPNet: End-to-End Perception and Prediction with Tracking in the Loop.  [__`lidar`__] 

- [[CVPR](http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Train_in_Germany_Test_in_the_USA_Making_3D_Object_CVPR_2020_paper.pdf)] Train in Germany, Test in The USA: Making 3D Object Detectors Generalize.[[code](https://github.com/cxy1997/3D_adapt_auto_driving)] [__`lidar`__] 

- [[CVPR](https://arxiv.org/pdf/1911.10150)] PointPainting: Sequential Fusion for 3D Object Detection. [__`lidar+image`__] [__`kitti`__] [__`nusc`__] 

- [[CVPR](https://arxiv.org/pdf/2001.03398)]  DSGN: Deep Stereo Geometry Network for 3D Object Detection. [__`monocular`__] [__`kitti`__] 

- [[CVPR](https://arxiv.org/pdf/2004.03572)]  Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation.[[code](https://github.com/zju3dv/disprcnn)] [__`stereo`__] [__`kitti`__] 

- [[CVPR](https://arxiv.org/pdf/1912.04799)] Learning Depth-Guided Convolutions for Monocular 3D Object Detection.[[code](https://github.com/dingmyu/D4LCN)] [__`monocular`__] [__`kitti`__] 

- [[CVPR](https://arxiv.org/pdf/2003.00504)] MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. [__`monocular`__] [__`kitti`__] 

- [[CVPR](https://arxiv.org/pdf/2004.01389)] LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention. [__`lidar_video`__] [__`nusc`__] 

- [[CVPR](https://arxiv.org/pdf/2004.00543)] Physically Realizable Adversarial Examples for LiDAR Object Detection. [__`lidar`__] 

- [[CVPR](https://arxiv.org/pdf/2003.00186)]HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. [__`lidar`__] [__`kitti`__] 

- [[CVPR](https://arxiv.org/pdf/2004.01170)]Dops: Learning to detect 3d objects and predict their 3d shapes. [__`lidar_video`__] [__`waymo`__] 

- [[CVPR](https://arxiv.org/pdf/2004.08745)]Learning to Evaluate Perception Models Using Planner-Centric Metrics. [__`lidar`__]

- [[CVPR](https://arxiv.org/pdf/1912.04986)]What You See is What You Get: Exploiting Visibility for 3D Object Detection. [__`lidar`__] [__`nusc`__] 

- [[CVPR](https://arxiv.org/pdf/2003.06754)]MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps. [__`lidar`__] 

- [[ECCVW](https://arxiv.org/pdf/2008.08766)]  Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations.[[code](https://github.com/AutoVision-cloud/Deformable-PV-RCNN)][__`lidar`__] [__`kitti`__] 

- [[ECCV](http://scholar.google.de/scholar?q=object%20as%20hotspots)]  object as hotspots.[__`lidar`__] [__`kitti`__] 

- [[ECCV](https://arxiv.org/pdf/2007.08856)]   EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection.[__`lidar+image`__] [__`kitti`__] 

- [[ECCV](https://arxiv.org/pdf/2004.12636)]   3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection.[__`lidar+image`__] [__`kitti`__] 

- [[ECCV](https://arxiv.org/pdf/2007.09548)]    Kinematic 3D Object Detection in Monocular Video.[[code](http://cvlab.cse.msu.edu/project-kinematic.html)][__`monocular_video`__] [__`kitti`__] 

- [[ECCV](https://arxiv.org/pdf/2008.04582)]   Rethinking Pseudo-LiDAR Representation.[[code](https://github.com/xinzhuma/patchnet)][__`monocular`__] [__`kitti`__] 

- [[ECCV](https://arxiv.org/pdf/2007.12392)]  An LSTM Approach to Temporal 3D Object Detection in LiDAR Point Clouds.[__`lidar`__] [__`waymo`__] 

- [[ECCV](https://arxiv.org/pdf/2007.10323)]  Pillar-based Object Detection for Autonomous Driving.[__`lidar`__] [__`waymo`__] 

- [[ECCV](https://arxiv.org/pdf/2008.02191.pdf)]  Active Perception using Light Curtains for Autonomous Driving.[[code](http://siddancha.github.io/projects/active-perception-light-curtains)][__`lidar`__] 

- [[ECCV](https://arxiv.org/pdf/2007.16100.pdf)]  Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution.[__`lidar`__] 

- [[ECCV](https://arxiv.org/pdf/2004.00831)]  Improving 3D Object Detection through Progressive Population Based Augmentation.[__`lidar`__] [__`kitti`__] 

- [[IROS](https://arxiv.org/pdf/2006.05518)] MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views.[__`lidar`__] [__`nusc`__] 

- [[ACMMM](https://arxiv.org/pdf/2007.13970.pdf)] Weakly Supervised 3D Object Detection from Point Clouds.[__`lidar`__]

- [[BMVC](https://arxiv.org/pdf/2005.10863)] RV-FuseNet: Range View based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting [__`lidar`__][__`nusc`__]

- [[Sensors]()] 3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2004.04962)] 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2006.11275)] Center-based 3D Object Detection and Tracking [[code](https://github.com/tianweiy/CenterPoint)][__`lidar`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2004.00186)] Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds [__`lidar`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2007.08556)] InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [__`lidar`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2004.01643)] Quantifying Data Augmentation for LiDAR based 3D Object Detection [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2005.09927)] Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection [__`lidar`__][__`kitti`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2003.10670)] Real-time 3D object proposal generation and classification under limited processing resources [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2003.11242)] Safety-Aware Hardening of 3D Object Detection Neural Network Systems [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2006.05187)] Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection[__`stereo`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2004.02774)] SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds [[code](https://github.com/xinge008/SSN)][__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2006.04043)] SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds

[__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2008.12008.pdf)] GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2008.10436.pdf)] Cross-Modality 3D Object Detection [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2008.09672)] Towards Autonomous Driving: a Multi-Modal 360∘ Perception Proposal[__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.00206)]  RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation[__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.00784.pdf)] CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection[__`lidar+image`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2008.13748)]Reinforced Axial Refinement Network for Monocular 3D Object Detection[__`monocular`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2008.12599.pdf)]PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges[__`lidar`__][__`kitti`__][__`waymo`__]

- [[arxiv](https://arxiv.org/pdf/2008.12008.pdf)]GhostBuster: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2007.07214)]CenterNet3D:An Anchor free Object Detector for Autonomous Driving [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2007.13373.pdf)] Part-Aware Data Augmentation for 3D Object Detection in Point Cloud. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2006.15505.pdf)] 1st Place Solution for Waymo Open Dataset Challenge - 3D Detection and Domain Adaptation. [__`lidar`__][__`waymo`__]

- [[arxiv](https://arxiv.org/pdf/2009.04554.pdf)] RoIFusion: 3D Object Detection from LiDAR and Vision. [__`lidar+image`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.05307.pdf)] A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.08428.pdf)] Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles

. [__`lidar+image`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2009.06169.pdf)] 3D Object Detection and Tracking Based on Streaming Data. [__`lidar`__][__`kitti`__]['det_and_tracking']

- [[arxiv](https://arxiv.org/pdf/2011.02553.pdf)] Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss. [__`lidar`__][__`kitti`__]['det_and_tracking']

- [[arxiv](https://arxiv.org/pdf/2011.01404.pdf)] Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2011.00652.pdf)] Multi-View Adaptive Fusion Network for 3D Object Detection. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2010.14599)] Stereo Frustums: A Siamese Pipeline for 3D Object Detection. [__`stereo`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2010.11702.pdf)]MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving. [__`lidar`__]

- [[arxiv](https://arxiv.org/pdf/2010.08243)]SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection. [__`lidar`__][__`kitti`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2010.08243)]Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels. [__`mono`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.12276.pdf)]SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.11975.pdf)]CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2009.11859.pdf)]Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection. [__`lidar`__][__`waymo`__]

- [[arxiv](https://arxiv.org/pdf/2009.10945.pdf)] MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2011.04841)] CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection. [__`radar+image`__][__`nusc`__]

- [[arxiv](https://arxiv.org/pdf/2011.02553)] Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2011.01630)] A Neural Network for Fast and Efficient Edge Detection in 3D Point Clouds. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2012.03015)] CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. [__`lidar`__][__`kitti`__]

- [[arxiv](https://arxiv.org/pdf/2012.03121)] It’s All Around You: Range-Guided Cylindrical Network for 3D Object Detection. [__`lidar`__][__`kitti`__]

## Suvery

- [[TPAMI](https://arxiv.org/pdf/1912.12033)] Deep Learning for 3D Point Clouds: A Survey[__`lidar`__]

- [[arxiv](https://arxiv.org/pdf/2003.00601)] 3D Point Cloud Processing and Learning for Autonomous Driving[__`lidar`__]

- [[arxiv](https://arxiv.org/pdf/2009.08920.pdf)] Deep Learning for 3D Point Cloud Understanding: A Survey[__`lidar`__]

- [[arxiv](https://arxiv.org/pdf/2010.15614.pdf)] An Overview Of 3D Object Detection[__`lidar`__]



## code base 

- [[lidar_only](https://github.com/traveller59/second.pytorch)] second.pytorch [__`kitti`__][__`nusc`__]

- [[lidar_only](https://github.com/poodarchu/Det3D)] Det3D [__`kitti`__][__`nusc`__][__`lyft`__][__`waymo`__]

- [[lidar_only](https://github.com/open-mmlab/OpenPCDet)] OpenPCDet[__`kitti`__][__`nusc`__][__`waymo`__]

 - [[lidar_image](https://github.com/open-mmlab/mmdetection3d)] mmdetection3d[__`kitti`__][__`nusc`__][__`lyft`__][__`waymo`__]