https://github.com/beedotkiran/Lidar_For_AD_references

A list of references on lidar point cloud processing for autonomous driving
https://github.com/beedotkiran/Lidar_For_AD_references
autonomous-driving clustering lidar-point-cloud obstacle-detection simulator
Last synced: 26 days ago
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A list of references on lidar point cloud processing for autonomous driving
Host: GitHub
URL: https://github.com/beedotkiran/Lidar_For_AD_references
Owner: beedotkiran
Created: 2018-06-03T12:49:32.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2022-04-17T20:15:02.000Z (almost 3 years ago)
Last Synced: 2024-08-01T03:32:03.634Z (9 months ago)
Topics: autonomous-driving, clustering, lidar-point-cloud, obstacle-detection, simulator
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- Readme: README.md
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README

        # Lidar Point clound processing for Autonomous Driving

A list of references on lidar point cloud processing for autonomous driving

## LiDAR Pointcloud Clustering/Semantic Segmentation/Plane extraction 

**Tasks** : Road/Ground extraction, plane extraction, Semantic segmentation, open set instance segmentation, Clustering

* Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications ICRA 2017 [[git](https://github.com/VincentCheungM/Run_based_segmentation), [pdf](https://www.researchgate.net/publication/318325507_Fast_Segmentation_of_3D_Point_Clouds_A_Paradigm_on_LiDAR_Data_for_Autonomous_Vehicle_Applications)]

* Time-series LIDAR Data Superimposition for Autonomous Driving [[pdf](http://lab.cntl.kyutech.ac.jp/~nishida/paper/2016/ThBT3.3.pdf)]

* Fast segmentation of 3D point clouds for ground vehicles [[ieee](https://ieeexplore.ieee.org/document/5548059)]

* An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

* Segmentation of Dynamic Objects from Laser Data [[pdf](https://upcommons.upc.edu/bitstream/handle/2117/14119/1259-Segmentation-of-Dynamic-Objects-from-Laser-Data.pdf?sequence=1&isAllowed=y)]

* A Fast Ground Segmentation Method for 3D Point Cloud [[pdf](http://jips-k.org/file/down?pn=463)]

* Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field [[pdf](https://hal.inria.fr/hal-01579095/document)]

* Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA [[pdf](https://arxiv.org/pdf/1711.02757.pdf)]

* Efficient Online Segmentation for Sparse 3D Laser Scans [[pdf](http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi16pfg.pdf)], [[git](https://github.com/PRBonn/depth_clustering)]

* CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data [[pdf](https://arxiv.org/pdf/1709.02128.pdf)]

* A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds 2016 Masters Thesis [[pdf](http://publications.lib.chalmers.se/records/fulltext/238602/238602.pdf)]

* Fast Multi-pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles [pdf](https://ieeexplore.ieee.org/abstract/document/8500552) [video](https://www.youtube.com/watch?v=cwmcuRnWJfE)

* RangeNet++: Fast and Accurate LiDAR Semantic Segmentation [[link](https://github.com/PRBonn/lidar-bonnetal], [[pdf](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/milioto2019iros.pdf)]

* Circular Convolutional Neural Networks for Panoramic Images and Laser Data [pdf](https://www.tu-chemnitz.de/etit/proaut/publications/schubert19_IV.pdf)

* Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds [[pdf](http://www.cs.toronto.edu/~urtasun/publications/zhang_etal_3dv18.pdf)]

* Identifying Unknown Instances for Autonomous Driving/Open-set instance segmentation algorithm [CoRL 2019](https://www.robot-learning.org/) [[pdf](https://arxiv.org/abs/1910.11296)]

* RIU-Net: Embarrassingly simple semantic segmentation of3D LiDAR point cloud. [[pdf](https://arxiv.org/abs/1905.08748), [LU-net](https://hal.archives-ouvertes.fr/hal-02269915/document)]

* SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving [[pdf](https://arxiv.org/abs/1909.08291)]

* SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation [[link](https://github.com/chenfengxu714/SqueezeSegV3)]

* PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation [[link](https://arxiv.org/abs/2003.14032)]

* Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study IV 2020 [[pdf](https://arxiv.org/abs/2004.11803)]

* Plane Segmentation Based on the Optimal-vector-field in LiDAR Point Clouds [[link](https://ieeexplore.ieee.org/document/9095372)]

* Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning [[link](https://arxiv.org/abs/1809.00426)]

* Learning Hierarchical Semantic Segmentations of LIDAR Data 3DV 2015 [[pdf](https://www.cs.princeton.edu/~funk/3DV15.pdf   )]

* EfficientLPS: Efficient LiDAR Panoptic Segmentation 2021 [pdf](https://arxiv.org/abs/2102.08009), [video](https://www.youtube.com/watch?v=_ay7ci-Nd0E)

* 4D Panoptic LiDAR Segmentation 2021 [[pdf](https://arxiv.org/abs/2102.12472)]

* urban_road_filter: a real-time LIDAR-based urban road and sidewalk detection algorithm for autonomous vehicles [[git](https://github.com/jkk-research/urban_road_filter), [video](https://www.youtube.com/watch?v=T2qi4pldR-E), [pdf](https://www.mdpi.com/1424-8220/22/1/194/pdf), [code](https://github.com/jkk-research/urban_road_filter)]

## Pointcloud Density \& Compression

* DBSCAN : A density-based algorithm for discovering clusters in large spatial databases with noise (1996) [[pdf](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf)]

* Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection [pdf](https://bdpi.usp.br/bitstream/handle/BDPI/51005/2709770.pdf?sequence=1)

* Building Maps for Autonomous Navigation Using Sparse Visual SLAM Features [[pdf](https://ygling2008.github.io/papers/IROS2017.pdf)]

* STD: Sparse-to-Dense 3D Object Detector for Point Cloud [pdf](https://arxiv.org/abs/1907.10471)

* Fast semantic segmentation of 3d point clounds with strongly varying density [[pdf](https://www.ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/timo-jan-isprs2016.pdf)]

* The Perfect Match: 3D Point Cloud Matching with Smoothed Densities [[pdf](https://arxiv.org/abs/1811.06879), [code](https://github.com/zgojcic/3DSmoothNet)]

* Deep Compression for Dense Point Cloud Maps [[link](https://ieeexplore.ieee.org/document/9354895)]

* Improved Deep Point Cloud Geometry Compression [[pdf](https://hal.archives-ouvertes.fr/hal-02910180/document), [git](https://github.com/mauriceqch/pcc_geo_cnn_v2)]

* Real-Time Spatio-Temporal LiDAR Point Cloud Compression [[pdf](http://ras.papercept.net/images/temp/IROS/files/1091.pdf)]

## Registration and Localization

* A Review of Point Cloud Registration Algorithms for Mobile Robotics 2015 [[pdf](https://hal.archives-ouvertes.fr/hal-01178661/document)]

* LOAM: Lidar Odometry and Mapping in Real-time RSS 2014 [[pdf](https://ri.cmu.edu/pub_files/2014/7/Ji_LidarMapping_RSS2014_v8.pdf), [video](https://www.youtube.com/watch?v=8ezyhTAEyHs)]

* Fast Planar Surface 3D SLAM Using LIDAR 2016 [[pdf](https://lamor.fer.hr/images/50020776/Lenac2017.pdf)]

* Point Clouds Registration with Probabilistic Data Association IROS 2016 [[git](https://github.com/ethz-asl/robust_point_cloud_registration)]

* Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving IJRR 2017 [[pdf](https://pdfs.semanticscholar.org/7292/1fc6b181cf75790664e482963d982ec9ac48.pdf)], [[Thesis](https://pdfs.semanticscholar.org/a7ce/36bbdf85f1dba6cf16f47ad3799618511960.pdf)]

* Automatic Merging of Lidar Point-Clouds Using Data from Low-Cost GPS/IMU Systems SPIE 2011 [[pdf](https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1081&context=ece_facpub)]

* Fast and Robust 3D Feature Extraction from Sparse Point Clouds [[pdf](http://jacoposerafin.com/wp-content/uploads/serafin16iros.pdf)]

* 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [[pdf](https://arxiv.org/abs/1807.09413)]

* Incremental Segment-Based Localization in 3D Point Clouds [[pdf](http://www.gilitschenski.org/igor/publications/201807-ral-incremental_segmatch/ral18-incremental_segmatch.pdf)]

* OverlapNet: Loop Closing for LiDAR-based SLAM, RSS 2020 [[pdf](OverlapNet: Loop Closing for LiDAR-based SLAM), [git](https://github.com/PRBonn/OverlapNet), [video](https://www.youtube.com/watch?v=96TBjiay59A)]

* CorsNet: 3D Point Cloud Registration by Deep Neural Network, ICRA 2020 [[link](https://ieeexplore.ieee.org/abstract/document/8978671)]

* LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis ICCV 2019 [[pdf](https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_LPD-Net_3D_Point_Cloud_Learning_for_Large-Scale_Place_Recognition_and_ICCV_2019_paper.pdf)]

* DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization [[pdf](https://arxiv.org/abs/2007.09217),[project](https://vision.in.tum.de/research/vslam/dh3d), [video](https://www.youtube.com/watch?v=ZxZiwZugG14)]

* Localisation using LiDAR and Camera Localisation in low visibility road conditions Master’s thesis 2017 [[pdf](http://publications.lib.chalmers.se/records/fulltext/250431/250431.pdf)]

* Monocular Camera Localization in 3D LiDAR Maps IROS 2016 [[pdf](http://www.lifelong-navigation.eu/files/caselitz16iros.pdf)]

## Feature Extraction

* Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR [[pdf](https://www.ri.cmu.edu/pub_files/2008/9/peterson_kevin_2008_1.pdf)]

* Finding Planes in LiDAR Point Clouds for Real-Time Registration [[pdf](http://ilab.usc.edu/publications/doc/Grant_etal13iros.pdf)]

* Online detection of planes in 2D lidar [[pdf](https://pdfs.semanticscholar.org/6857/b602dd702664c20febd41dc984451fd97bb3.pdf)]

* A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements [[pdf](http://vision.ucla.edu/papers/fontanelliRS07.pdf)]

* Hierarchical Plane Extraction (HPE): An Efficient Method For Extraction Of Planes From Large Pointcloud Datasets [[pdf](https://pdfs.semanticscholar.org/8217/61a207088e6015de845cc3f9e556e1c94be1.pdf)]

* A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing [[pdf](https://hal-mines-paristech.archives-ouvertes.fr/hal-01097361/document)]

* SPLATNet: Sparse Lattice Networks for Point Cloud Processing CVPR 2018 [[pdf](https://arxiv.org/abs/1802.08275), [code](https://github.com/NVlabs/splatnet)]

* PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding, 4D-VISION workshop at ECCV'2020 [[pdf](https://drive.google.com/file/d/1J1KqRQlvMeLThevw_2Erms5IiZWkEwde/view?usp=sharing), [workshop](https://sites.google.com/view/4dvision#h.u1ymvub25j59)]

## Object detection and Tracking

* Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping [pdf](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.6290)

* Terrain-Adaptive Obstacle Detection [[pdf](https://pdfs.semanticscholar.org/92f6/26e75f940a49ee80eaf0344dc493f5d8b2ee.pdf)]

* 3D Object Detection from Roadside Data Using Laser Scanners [[pdf](http://www-video.eecs.berkeley.edu/papers/JYT/spie-paper.pdf)]

* 3D Multiobject Tracking for Autonomous Driving : Masters thesis A S Abdul Rahman

* Motion-based Detection and Tracking in 3D LiDAR Scans [[pdf](http://ais.informatik.uni-freiburg.de/publications/papers/dewan16icra.pdf)]

* Lidar-histogram for fast road and obstacle detection [[pdf](http://www.chenliang.me/blog/wp-content/uploads/2017/07/lidarhistogram.pdf)]

* End-to-end Learning of Multi-sensor 3D Tracking by Detection [pdf](https://arxiv.org/pdf/1806.11534.pdf)

* Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf](https://arxiv.org/abs/1809.05590)

* Deep tracking in the wild: End-to-end tracking using recurrent neural networks [[pdf](http://www.robots.ox.ac.uk/~mobile/Papers/2017_IJRR_Dequaire.pdf)]

* Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [[pdf](https://arxiv.org/abs/1809.05590)], [video](https://www.youtube.com/watch?v=2DzH9COLpkU)]

* VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection CVPR 2018 [[pdf](https://arxiv.org/abs/1711.06396), [code](https://github.com/tsinghua-rll/VoxelNet-tensorflow)]

* PIXOR: Real-time 3D Object Detection from Point Clouds CVPR 2018 [[pdf](https://arxiv.org/pdf/1902.06326.pdf)]

* Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges [[pdf](https://arxiv.org/pdf/1902.07830.pdf)]

* Low resolution lidar-based multi-object tracking for driving applications [[pdf](https://upcommons.upc.edu/bitstream/handle/2117/113342/1924-Low-resolution-lidar-based-multi-object-tracking-for-driving-applications.pdf)]

* Patch Refinement -- Localized 3D Object Detection [[pdf](https://arxiv.org/abs/1910.04093)]

* PointPillars: Fast Encoders for Object Detection from Point Clouds CVPR 2019 [[pdf](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.pdf)]

* StarNet: Targeted Computation for Object Detection in Point Clouds NeurIPS 2019 ML4AD [[pdf](https://arxiv.org/abs/1908.11069)]

* PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection CVPR 2020 [[pdf](http://openaccess.thecvf.com/content_CVPR_2020/papers/Shi_PV-RCNN_Point-Voxel_Feature_Set_Abstraction_for_3D_Object_Detection_CVPR_2020_paper.pdf)]

* LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving CVPR 2019 [[pdf](http://openaccess.thecvf.com/content_CVPR_2019/papers/Meyer_LaserNet_An_Efficient_Probabilistic_3D_Object_Detector_for_Autonomous_Driving_CVPR_2019_paper.pdf)]

* Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection 2020 [[pdf](https://arxiv.org/abs/2005.09927)]

* AFDet: Anchor Free One Stage 3D Object Detection [[pdf](https://arxiv.org/abs/2006.12671)]

* SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud (CVPR 2020) [[pdf](https://www4.comp.polyu.edu.hk/~cslzhang/paper/SA-SSD.pdf), [git](https://github.com/skyhehe123/SA-SSD)]

* Any Motion Detector: Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds, ICRA 2020 [[pdf](https://arxiv.org/abs/2004.11647)]

* MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views [[link](https://www.youtube.com/watch?v=2ck5_sToayc), [video](https://www.youtube.com/watch?v=2ck5_sToayc)]

* Learning to Optimally Segment Point Clouds, ICRA 2020 [[pdf](https://arxiv.org/abs/1912.04976), [video](https://www.youtube.com/watch?v=wLxIAwIL870), [git](https://github.com/peiyunh/opcseg)]

* What You See is What You Get: Exploiting Visibility for 3D Object Detection [[pdf](https://arxiv.org/abs/1912.04986), [video](https://www.youtube.com/watch?v=497OF-otY2k), [project](https://www.cs.cmu.edu/~peiyunh/wysiwyg/index.html)]

## Classification/Supervised Learning

* PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [[link](http://stanford.edu/~rqi/pointnet/), [link2](http://stanford.edu/~rqi/pointnet2/)]

* SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud [pdf](https://arxiv.org/pdf/1710.07368.pdf)

* Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes [[pdf](https://hal.archives-ouvertes.fr/hal-01182604/document)]

* DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [[pdf](http://home.isr.uc.pt/~cpremebida/files_cp/DepthCN_preprint.pdf)]

* 3D Object Localisation with Convolutional Neural Networks [[Thesis](https://github.com/oscarmcnulty/gta-3d-dataset/blob/master/3D-object-localisation-with-cnns.pdf)]

* SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud [[pdf](https://arxiv.org/pdf/1809.08495.pdf)]

* PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud [[pdf](https://arxiv.org/pdf/1807.06288.pdf)]

* Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks [[pdf](https://arxiv.org/abs/1703.03613)]

* ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA [[pdf](https://arxiv.org/pdf/1808.03506.pdf)]

## Maps / Grids / HD Maps / Occupancy grids/ Prior Maps

* Hierarchies of Octrees for Efficient 3D Mapping [pdf](https://www.ais.uni-bonn.de/papers/IROS-2011_Wurm_Holz.pdf)

* Adaptive Resolution Grid Mapping using Nd-Tree [[ieee](https://ieeexplore.ieee.org/document/5980084)], [[pdf](https://www.researchgate.net/publication/224252536_Finding_the_adequate_resolution_for_grid_mapping_-_Cell_sizes_locally_adapting_on-the-fly), [video](https://www.youtube.com/watch?v=PYMlo8Wb6qE)]

* LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles [[link](https://ieeexplore.ieee.org/document/8170357/)]

* Long-term robot mapping in dynamic environments, Aisha Naima Walcott Thesis MIT 2011 [[link](https://dspace.mit.edu/handle/1721.1/66468)]

* Long-term 3D map maintenance in dynamic environments ICRA 2014 [[pdf](https://hal.archives-ouvertes.fr/hal-01143106/file/2014_Pomerleau_ICRA_Long-term.pdf), [video](https://www.youtube.com/watch?v=cMgLyLpnsoU)]

* Detection and Tracking of Moving Objects Using 2.5D Motion Grids ITSC 2015 [[pdf](http://a-asvadi.ir/wp-content/uploads/itsc15.pdf)]

* Autonomous Vehicle Navigation in Rural Environments without Detailed Prior Maps ICRA 2018 [[pdf](https://toyota.csail.mit.edu/sites/default/files/documents/papers/ICRA2018_AutonomousVehicleNavigationRuralEnvironment.pdf), [video](https://www.youtube.com/watch?v=v4qVNcGoMnI)]

* 3D Lidar-based Static and Moving Obstacle Detection in Driving Environments: an approach based on voxels and multi-region ground planes [[pdf](http://patternrecognition.cn/perception/negative2016a.pdf)]

* Spatio–Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments [[pdf](https://papers.nips.cc/paper/6541-spatio-temporal-hilbert-maps-for-continuous-occupancy-representation-in-dynamic-environments.pdf)]

* Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling [[pdf](https://arxiv.org/pdf/1705.08781.pdf)]

* Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review [[pdf](https://www.mdpi.com/2072-4292/10/10/1531)]

* Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping [[pdf](https://www.ais.uni-bonn.de/papers/ICRA_2018_Droeschel.pdf)]

* DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_DeLS-3D_Deep_Localization_CVPR_2018_paper.pdf)],[video](https://www.youtube.com/watch?v=M6lhkzKFEhA)]

* Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data [[pdf](https://arxiv.org/pdf/1807.00925.pdf)]

* HDNET: Exploiting HD Maps for 3D Object Detection [[pdf](http://proceedings.mlr.press/v87/yang18b/yang18b.pdf)]

* Mapping with Dynamic-Object Probabilities Calculated from Single 3D Range Scans ICRA 2018 [[pdf](http://ais.informatik.uni-freiburg.de/publications/papers/ruchti18icra.pdf)]

## End-To-End Learning

* Monocular Fisheye Camera Depth Estimation Using Semi-supervised Sparse Velodyne Data [[pdf](https://arxiv.org/pdf/1803.06192.pdf)]

* Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf)]

## Simulated pointclouds / Simulators 

* Virtual Generation of Lidar Data for Autonomous Vehicles Simulation of a lidar sensor inside a virtual world Bachelors Thesis 2017 [pdf](https://gupea.ub.gu.se/bitstream/2077/53342/1/gupea_2077_53342_1.pdf)

* A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving ACM 2018 [[pdf](https://arxiv.org/abs/1804.00103)]

* Udacity based simulator [[link](http://wangyangevan.weebly.com/lidar-simulation.html), [git](https://github.com/EvanWY/USelfDrivingSimulator)]

* Tutorial on Gazebo to simulate raycasting from Velodyne lidar [[link](http://gazebosim.org/tutorials?tut=guided_i1)]

* Udacity Driving Dataset [[link](https://github.com/udacity/self-driving-car/tree/master/datasets)]

* Virtual KITTI [[link](http://www.europe.naverlabs.com/Research/Computer-Vision/Proxy-Virtual-Worlds)]

* SynthCity: A large-scale synthetic point cloud 2019 [[dataset](http://www.synthcity.xyz/), [pdf](https://arxiv.org/abs/1907.04758)]

* Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception [[link](https://uwaterloo.ca/waterloo-intelligent-systems-engineering-lab/projects/precise-synthetic-image-and-lidar-presil-dataset-autonomous)]

* Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images 2020 [[pdf](https://arxiv.org/abs/2006.04345)]

## Lidar Datasets 

* nuScenes : public large-scale dataset for autonomous driving [[dataset](https://www.nuscenes.org/overview)]

    * nuScenes-lidarseg will be released in Q2 2020. [[link](https://www.nuscenes.org/lidarseg)]

* Ford Campus Vision and Lidar Data Set [[pdf](http://robots.engin.umich.edu/uploads/SoftwareData/Ford/ijrr2011.pdf), [dataset](http://robots.engin.umich.edu/SoftwareData/Ford)]

* Oxford RobotCar dataset [dataset](https://robotcar-dataset.robots.ox.ac.uk/) 1 Year, 1000km: The Oxford RobotCar Dataset [pdf](https://robotcar-dataset.robots.ox.ac.uk/images/robotcar_ijrr.pdf)

* LiDAR-Video Driving Dataset: Learning Driving Policies Effectively [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_LiDAR-Video_Driving_Dataset_CVPR_2018_paper.pdf)]

* KAIST Complex Urban Data Set Dataset [[dataset](http://irap.kaist.ac.kr/dataset/download_1.html)]

* Semantic 3D 2017 [dataset](http://www.semantic3d.net/) 

* Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification [[pdf](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w40/Roynard_Paris-Lille-3D_A_Point_CVPR_2018_paper.pdf) [dataset](http://npm3d.fr/paris-lille-3d)]

* Semantic KITTI 2019 [[dataset](http://semantic-kitti.org/)]

* A*3D: An Autonomous Driving Dataset in Challeging Environments [[dataset](https://github.com/I2RDL2/ASTAR-3D)], [video](https://www.youtube.com/watch?v=QtK0VIywrmM&feature=youtu.be)]

* HD Map Dataset & Localization Dataset NAVER Labs : [[link](https://hdmap.naverlabs.com/dataset.html)]

* Argoverse by ARGO AI : Two public datasets supported by highly detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them. [[link](https://www.argoverse.org/)]

* Lyft dataset [[link](https://level5.lyft.com/dataset/)]

* SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances [[link](http://www.poss.pku.edu.cn/semanticposs.html), [pdf](https://arxiv.org/abs/2002.09147)]

* A2D2 Audi dataset [[link](https://www.a2d2.audi/a2d2/en/dataset.html)]

* PandaSet : Public large-scale dataset for autonomous driving provided by Hesai & Scale. [[link](https://scale.com/open-datasets/pandaset)]

* Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways [[pdf](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w11/Tan_Toronto-3D_A_Large-Scale_Mobile_LiDAR_Dataset_for_Semantic_Segmentation_of_CVPRW_2020_paper.pdf), [dataset](https://github.com/WeikaiTan/Toronto-3D)]

## Spatio-Temporal, Movement, Flow estimation in Pointclouds

* Rigid Scene Flow for 3D LiDAR Scans IROS 2016 [[pdf](https://europa2.informatik.uni-freiburg.de/files/dewan-16iros.pdf)]

* Deep Lidar CNN to Understand the Dynamics of Moving Vehicles [[pdf](http://www.iri.upc.edu/files/scidoc/2018-Deep-Lidar-CNN-to-Understand-the-Dynamics-of-Moving-Vehicles.pdf)]

* Learning motion field of LiDAR point cloud with convolutional networks [[link](https://www.sciencedirect.com/science/article/abs/pii/S016786551930176X)]

* Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles [[pdf](https://arxiv.org/pdf/1808.10542.pdf) [video](https://www.youtube.com/watch?v=94vQUwCZLxQ)]

* FlowNet3D: Learning Scene Flow in 3D Point Clouds CVPR 2019 [[pdf](https://zpascal.net/cvpr2019/Liu_FlowNet3D_Learning_Scene_Flow_in_3D_Point_Clouds_CVPR_2019_paper.pdf), [code](https://github.com/xingyul/flownet3d)]

* LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images [[pdf](https://arxiv.org/abs/1910.14453v1)]

* 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks CVPR 2019 [[pdf](https://zpascal.net/cvpr2019/Choy_4D_Spatio-Temporal_ConvNets_Minkowski_Convolutional_Neural_Networks_CVPR_2019_paper.pdf), [code](https://github.com/StanfordVL/MinkowskiEngine)]

* MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences, ICCV 2019 [[pdf](http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.pdf)]

* DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR 2020 [[pdf](https://arxiv.org/abs/2008.08136)]

## Advanced Topics/Other applications

**Tasks** : Upsampling, Domain adaptation Sim2Real, NAS, SSL, shape reconstruction, outlier extraction, Compression, Change detection, Domain Transfer

* Semantic Point Cloud Filtering, Masters thesis 2017 [link](https://ethz.ch/content/dam/ethz/special-interest/baug/igp/photogrammetry-remote-sensing-dam/documents/pdf/Student_Theses/MasterThesis_Stucker.pdf)

* PU-GAN: a Point Cloud Upsampling Adversarial Network ICCV 2019 [[pdf](https://arxiv.org/abs/1907.10844), [code](https://github.com/liruihui/PU-GAN)]

* Neural Architecture Search for Object Detection in Point Cloud [[blog](https://medium.com/seoul-robotics/neural-architecture-search-for-object-detection-in-point-cloud-f2d57a5953d5)], [[AutoDeepLabNAS paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Auto-DeepLab_Hierarchical_Neural_Architecture_Search_for_Semantic_Image_Segmentation_CVPR_2019_paper.pdf)]

* Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance [[pdf](https://arxiv.org/abs/1904.04094)]

* Quantifying Data Augmentation for LiDAR based 3D Object Detection [[pdf](https://arxiv.org/pdf/2004.01643v1.pdf)]

* Improving 3D Object Detection through Progressive Population Based Augmentation [[pdf(https://arxiv.org/abs/2004.00831)]

* 3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction VEHITS 2020 [[pdf](https://arxiv.org/abs/2003.00888)]

* Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution ITSC 2019 [[pdf](https://arxiv.org/abs/1905.03066)]

* Performance of LiDAR object detection deep learning architectures based on artificially generated point cloud data from CARLA simulator 2019 [[pdf](https://ieeexplore.ieee.org/abstract/document/8864642/)]

* Efficient Learning on Point Clouds with Basis Point Sets ICCV 2019 [[pdf](https://openaccess.thecvf.com/content_ICCV_2019/papers/Prokudin_Efficient_Learning_on_Point_Clouds_With_Basis_Point_Sets_ICCV_2019_paper.pdf)]

* Mastering Data Complexity for Autonomous Driving with Adaptive Point Clouds for Urban Environments 2017 [[pdf](https://www.researchgate.net/publication/318093493_Mastering_Data_Complexity_for_Autonomous_Driving_with_Adaptive_Point_Clouds_for_Urban_Environments)]

* Visually aided changes detection in 3D lidar based reconstruction 2015 [[Thesis](https://www.politesi.polimi.it/bitstream/10589/112343/3/tesi-Postica.pdf)]

* Fast Polar Attentive 3D Object Detection on LiDAR Point Clouds ML4AD 2021 [pdf](https://ml4ad.github.io/files/papers2021/Fast%20Polar%20Attentive%203D%20Object%20Detection%20on%20LiDAR%20Point%20Clouds.pdf)

## Implicit representations

* Neural Implicit Embedding for Point Cloud Analysis CVPR 2020 [[pdf](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fujiwara_Neural_Implicit_Embedding_for_Point_Cloud_Analysis_CVPR_2020_paper.pdf)]

* DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation [[pdf](https://arxiv.org/abs/1901.05103)]

## Domain adaptation and domain transer

* Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds 2020 [[pdf](https://arxiv.org/abs/2007.08488)]

* PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation [[pdf](https://papers.nips.cc/paper/8940-pointdan-a-multi-scale-3d-domain-adaption-network-for-point-cloud-representation.pdf)]

* Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks IROS 2020 [[pdf](http://ras.papercept.net/images/temp/IROS/files/0060.pdf), [video](https://www.youtube.com/watch?v=EkCO36zX6OI)]

* Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data ICCVW 2019 [pdf](http://openaccess.thecvf.com/content_ICCVW_2019/papers/TASK-CV/Saleh_Domain_Adaptation_for_Vehicle_Detection_from_Birds_Eye_View_LiDAR_ICCVW_2019_paper.pdf)

## Self-supervised learning

* Self-Supervised Deep Learning on Point Clouds by Reconstructing Space NeurIPS 2019 [[pdf](https://arxiv.org/abs/1901.08396)]

* SegContrast: 3D Point Cloud Feature Representation Learning RAL-ICRA'22 [video](https://www.youtube.com/watch?v=kotRb_ySnIw), [pdf](http://www.ipb.uni-bonn.de/pdfs/nunes2022ral-icra.pdf)

* Self-supervised Point Cloud Prediction Using 3D Spatio-temporal CNNs CORL 2021 [pdf](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2021corl.pdf) 

## Graphs and Pointclouds

* Detection of closed sharp edges in point clouds using normal estimation and graph theory CAD 2007 [[link](https://www.sciencedirect.com/science/article/abs/pii/S0010448506002260)]

* Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs CVPR 2017 [[pdf](https://openaccess.thecvf.com/content_cvpr_2017/papers/Simonovsky_Dynamic_Edge-Conditioned_Filters_CVPR_2017_paper.pdf), [vide](https://www.youtube.com/watch?v=THOoeNMwUIk)]

* Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs CVPR2018 [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers/Landrieu_Large-Scale_Point_Cloud_CVPR_2018_paper.pdf)]

* ConvPoint: continuous convolutions for cloud processing Eurographics 3DOR, 2019 [[pdf](https://arxiv.org/abs/1904.02375), [code](https://github.com/aboulch/ConvPoint)]

* Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning [[CVPR Workshop 2019](https://scene-understanding.com/papers/point_cloud_oversegmentation__CVPR_workshop_-1.pdf)], [video](https://www.youtube.com/watch?v=bKxU03tjLJ4&feature=youtu.be)

* Dynamic Graph CNN for Learning on Point Clouds [[pdf](https://arxiv.org/abs/1801.07829), [project](https://liuziwei7.github.io/projects/DGCNN)] TOG 2019

## Large-scale pointcloud Algorithms (vs scan based)

* Deep Parametric Continuous Convolutional Neural Networks CVPR 2018 [[pdf](http://openaccess.thecvf.com/content_cvpr_2018/papers_backup/Wang_Deep_Parametric_Continuous_CVPR_2018_paper.pdf)]

* PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space NeurIPS 2017 [[pdf](https://papers.nips.cc/paper/7095-pointnet-deep-hierarchical-feature-learning-on-point-sets-in-a-metric-space.pdf), [code](https://github.com/charlesq34/pointnet2)], [semantic seg code](https://github.com/mathieuorhan/pointnet2_semantic)

* Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network [Slides](https://project.inria.fr/ppniv18/files/2018/10/presentation.pdf)

* Semantic Segmentation of 3D point Clouds Loic Landireu [[Slides](http://bezout.univ-paris-est.fr/wp-content/uploads/2019/04/Landrieu_GT_appr_opt.pdf)]

* KPConv: Flexible and Deformable Convolution for Point Clouds [[pdf](https://arxiv.org/abs/1904.08889), [git](https://github.com/HuguesTHOMAS/KPConv)]

* RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds [[pdf](https://openaccess.thecvf.com/content_CVPR_2020/html/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.html), [git](https://github.com/QingyongHu/RandLA-Net)]

* Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation CVPR 2021 [pdf](https://arxiv.org/abs/2008.01550)

## 4D Panoptic LiDAR Segmentation

* Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking 2021 [video](https://www.youtube.com/watch?v=CG5dQbISK1g), [pdf](https://arxiv.org/abs/2109.03805), nuscenes [benchmark](https://www.nuscenes.org/panoptic?externalData=all&mapData=all&modalities=Any)

* 4D Panoptic LiDAR Segmentation CVPR 2021 [pdf](https://openaccess.thecvf.com/content/CVPR2021/papers/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.pdf), [project](https://mehmetaygun.github.io/4DPLS.html)

* Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans 2022 RAL [pdf](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/marcuzzi2022ral.pdf), [video](https://www.youtube.com/watch?v=XXwiadwjLp4)

## Embedded/Resource constrained pointcloud processing

* Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems [pdf](https://vedder.io/publications/sparse_point_pillars_iros_2022.pdf), [video](https://www.youtube.com/watch?v=zuLboHg3GLA)

* IBB-Net: Fast Iterative Bounding Box Regression for Detection on Point Clouds, Brendan Miller, 2020 CMU [masters thesis](https://www.ri.cmu.edu/wp-content/uploads/2020/06/MSR_Thesis.pdf)

* Fast 3-D Urban Object Detection on Streaming Point Clouds [[pdf](https://projet.liris.cnrs.fr/imagine/pub/proceedings/ECCV-2014/workshops/w15/Paper%202.pdf)]

* StrObe: Streaming Object Detection from LiDAR Packets [pdf](https://arxiv.org/abs/2011.06425), [video](https://www.youtube.com/watch?v=18uY9P6Y9Ys)

* PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars NeurIPS 2021 [pdf](https://arxiv.org/abs/2106.07545), [video](https://youtu.be/wGgO8SKwixo?t=1199), [code](https://github.com/motional/polarstream)

* RAD: Realtime and Accurate 3D Object Detection on Embedded Systems CVPRW 2021 [pdf](https://openaccess.thecvf.com/content/CVPR2021W/WAD/papers/Aghdam_RAD_Realtime_and_Accurate_3D_Object_Detection_on_Embedded_Systems_CVPRW_2021_paper.pdf)

* BEVDetNet: Bird’s Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving ITSC 2021 [pdf](https://arxiv.org/abs/2104.10780)

* Streaming Object Detection for 3-D Point Clouds 2020 [pdf](https://arxiv.org/abs/2005.01864)

 

## Tools/SW/Packages

* Python bindings for Point Cloud Library [[git](https://github.com/strawlab/python-pcl)] 

* Open3D [[link](http://www.open3d.org/)]

* pyntcloud [[link](https://github.com/daavoo/pyntcloud)]

* PyVista [[link](https://github.com/pyvista/pyvista)]

* torch-points3d : Pytorch framework for doing deep learning on point clouds  [[link](https://torch-points3d.readthedocs.io/en/latest/)]

* Geometric Deep Learning Extension Library for [PyTorch [link](https://pytorch-geometric.readthedocs.io/en/latest/)]

* kaolin : A PyTorch Library for Accelerating 3D Deep Learning Research [[link](https://github.com/NVIDIAGameWorks/kaolin/)]

* PyTorch3D : FAIR's library of reusable components for deep learning with 3D data [[link](https://pytorch3d.org/)]

* PCDet Toolbox in PyTorch for 3D Object Detection from Point Cloud [[link](https://github.com/sshaoshuai/PCDet)]

* pointcloudset: Efficient analysis of large datasets of point clouds recorded over time [[link](https://github.com/virtual-vehicle/pointcloudset)]
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