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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
，
］
Time-series LIDAR Data Superimposition for Autonomous Driving ［
］
Fast segmentation of 3D point clouds for ground vehicles ［
ieee
］
An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
Segmentation of Dynamic Objects from Laser Data ［
］
A Fast Ground Segmentation Method for 3D Point Cloud ［
］
Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field ［
］
Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA ［
］
Efficient Online Segmentation for Sparse 3D Laser Scans ［
］， ［
git
］
CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data ［
］
A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds 2016 Masters Thesis ［
］
Fast Multi-pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles
video
RangeNet++： Fast and Accurate LiDAR Semantic Segmentation ［［link］（
https：//
github。com/PRBonn/lidar
-bonnetal
］， ［
］
Circular Convolutional Neural Networks for Panoramic Images and Laser Data
Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds ［
］
Identifying Unknown Instances for Autonomous Driving/Open-set instance segmentation algorithm
CoRL 2019
［
］
RIU-Net： Embarrassingly simple semantic segmentation of3D LiDAR point cloud。 ［
，
LU-net
］
SalsaNet： Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving ［
］
SqueezeSegV3： Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation ［
link
］
PolarNet： An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation ［
link
］
Scan-based Semantic Segmentation of LiDAR Point Clouds： An Experimental Study IV 2020 ［
］
Plane Segmentation Based on the Optimal-vector-field in LiDAR Point Clouds ［
link
］
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning ［
link
］
Learning Hierarchical Semantic Segmentations of LIDAR Data 3DV 2015 ［
］
EfficientLPS： Efficient LiDAR Panoptic Segmentation 2021
，
video
4D Panoptic LiDAR Segmentation 2021 ［
］
Pointcloud Density & Compression
DBSCAN ： A density-based algorithm for discovering clusters in large spatial databases with noise （1996） ［
］
Hierarchical Density Estimates for Data Clustering， Visualization， and Outlier Detection
Building Maps for Autonomous Navigation Using Sparse Visual SLAM Features ［
］
STD： Sparse-to-Dense 3D Object Detector for Point Cloud
Fast semantic segmentation of 3d point clounds with strongly varying density ［
］
The Perfect Match： 3D Point Cloud Matching with Smoothed Densities ［
，
code
］
Deep Compression for Dense Point Cloud Maps ［
link
］
Improved Deep Point Cloud Geometry Compression ［
，
git
］
Real-Time Spatio-Temporal LiDAR Point Cloud Compression ［
］
Registration and Localization
A Review of Point Cloud Registration Algorithms for Mobile Robotics 2015 ［
］
LOAM： Lidar Odometry and Mapping in Real-time RSS 2014 ［
，
video
］
Fast Planar Surface 3D SLAM Using LIDAR 2016 ［
］
Point Clouds Registration with Probabilistic Data Association IROS 2016 ［
git
］
Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving IJRR 2017 ［
］， ［
Thesis
］
Automatic Merging of Lidar Point-Clouds Using Data from Low-Cost GPS/IMU Systems SPIE 2011 ［
］
Fast and Robust 3D Feature Extraction from Sparse Point Clouds ［
］
3DFeat-Net： Weakly Supervised Local 3D Features for Point Cloud Registration ［
］
Incremental Segment-Based Localization in 3D Point Clouds ［
］
OverlapNet： Loop Closing for LiDAR-based SLAM， RSS 2020 ［［pdf］（OverlapNet： Loop Closing for LiDAR-based SLAM），
git
，
video
］
CorsNet： 3D Point Cloud Registration by Deep Neural Network， ICRA 2020 ［
link
］
LPD-Net： 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis ICCV 2019 ［
］
DH3D： Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization ［
，
project
，
video
］
Localisation using LiDAR and Camera Localisation in low visibility road conditions Master’s thesis 2017 ［
］
Monocular Camera Localization in 3D LiDAR Maps IROS 2016 ［
］
Feature Extraction
Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR ［
］
Finding Planes in LiDAR Point Clouds for Real-Time Registration ［
］
Online detection of planes in 2D lidar ［
］
A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements ［
］
Hierarchical Plane Extraction （HPE）： An Efficient Method For Extraction Of Planes From Large Pointcloud Datasets ［
］
A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing ［
］
SPLATNet： Sparse Lattice Networks for Point Cloud Processing CVPR 2018 ［
，
code
］
PointContrast： Unsupervised Pre-training for 3D Point Cloud Understanding， 4D-VISION workshop at ECCV‘2020 ［
，
workshop
］
Object detection and Tracking
Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping
Terrain-Adaptive Obstacle Detection ［
］
3D Object Detection from Roadside Data Using Laser Scanners ［
］
3D Multiobject Tracking for Autonomous Driving ： Masters thesis A S Abdul Rahman
Motion-based Detection and Tracking in 3D LiDAR Scans ［
］
Lidar-histogram for fast road and obstacle detection ［
］
End-to-end Learning of Multi-sensor 3D Tracking by Detection
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
Deep tracking in the wild： End-to-end tracking using recurrent neural networks ［
］
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection ［
］，
video
］
VoxelNet： End-to-End Learning for Point Cloud Based 3D Object Detection CVPR 2018 ［
，
code
］
PIXOR： Real-time 3D Object Detection from Point Clouds CVPR 2018 ［
］
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving： Datasets， Methods， and Challenges ［
］
Low resolution lidar-based multi-object tracking for driving applications ［
］
Patch Refinement —— Localized 3D Object Detection ［
］
PointPillars： Fast Encoders for Object Detection from Point Clouds CVPR 2019 ［
］
StarNet： Targeted Computation for Object Detection in Point Clouds NeurIPS 2019 ML4AD ［
］
PV-RCNN： Point-Voxel Feature Set Abstraction for 3D Object Detection CVPR 2020 ［
］
LaserNet： An Efficient Probabilistic 3D Object Detector for Autonomous Driving CVPR 2019 ［
］
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection 2020 ［
］
AFDet： Anchor Free One Stage 3D Object Detection ［
］
SA-SSD： Structure Aware Single-stage 3D Object Detection from Point Cloud （CVPR 2020） ［
，
git
］
Any Motion Detector： Learning Class-agnostic Scene Dynamics from a Sequence of LiDAR Point Clouds， ICRA 2020 ［
］
MVLidarNet： Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views ［
link
，
video
］
Learning to Optimally Segment Point Clouds， ICRA 2020 ［
，
video
，
git
］
What You See is What You Get： Exploiting Visibility for 3D Object Detection ［
，
video
，
project
］
Classification/Supervised Learning
PointNet： Deep Learning on Point Sets for 3D Classification and Segmentation ［
link
，
link2
］
SqueezeSeg： Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes ［
］
DepthCN： Vehicle Detection Using 3D-LIDAR and ConvNet ［
］
3D Object Localisation with Convolutional Neural Networks ［
Thesis
］
SqueezeSegV2： Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud ［
］
PointSeg： Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud ［
］
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks ［
］
ChipNet： Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA ［
］
Maps / Grids / HD Maps / Occupancy grids/ Prior Maps
Hierarchies of Octrees for Efficient 3D Mapping
Adaptive Resolution Grid Mapping using Nd-Tree ［
ieee
］， ［
，
video
］
LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles ［
link
］
Long-term robot mapping in dynamic environments， Aisha Naima Walcott Thesis MIT 2011 ［
link
］
Long-term 3D map maintenance in dynamic environments ICRA 2014 ［
，
video
］
Detection and Tracking of Moving Objects Using 2。5D Motion Grids ITSC 2015 ［
］
Autonomous Vehicle Navigation in Rural Environments without Detailed Prior Maps ICRA 2018 ［
，
video
］
3D Lidar-based Static and Moving Obstacle Detection in Driving Environments： an approach based on voxels and multi-region ground planes ［
］
Spatio–Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments ［
］
Dynamic Occupancy Grid Prediction for Urban Autonomous Driving： A Deep Learning Approach with Fully Automatic Labeling ［
］
Fast 3-D Urban Object Detection on Streaming Point Clouds ［
］
Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction： A Review ［
］
Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping ［
］
DeLS-3D： Deep Localization and Segmentation with a 3D Semantic Map ［
］，
video
］
Recurrent-OctoMap： Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data ［
］
HDNET： Exploiting HD Maps for 3D Object Detection ［
］
Mapping with Dynamic-Object Probabilities Calculated from Single 3D Range Scans ICRA 2018 ［
］
End-To-End Learning
Monocular Fisheye Camera Depth Estimation Using Semi-supervised Sparse Velodyne Data ［
］
Fast and Furious： Real Time End-to-End 3D Detection， Tracking and Motion Forecasting with a Single Convolutional Net ［
］
Simulated pointclouds / Simulators
Virtual Generation of Lidar Data for Autonomous Vehicles Simulation of a lidar sensor inside a virtual world Bachelors Thesis 2017
A LiDAR Point Cloud Generator： from a Virtual World to Autonomous Driving ACM 2018 ［
］
Udacity based simulator ［
link
，
git
］
Tutorial on Gazebo to simulate raycasting from Velodyne lidar ［
link
］
Udacity Driving Dataset ［
link
］
Virtual KITTI ［
link
］
SynthCity： A large-scale synthetic point cloud 2019 ［
dataset
，
］
Precise Synthetic Image and LiDAR （PreSIL） Dataset for Autonomous Vehicle Perception ［
link
］
Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images 2020 ［
］
Lidar Datasets
nuScenes ： public large-scale dataset for autonomous driving ［
dataset
］
nuScenes-lidarseg will be released in Q2 2020。 ［
link
］
Ford Campus Vision and Lidar Data Set ［
，
dataset
］
Oxford RobotCar dataset
dataset
1 Year， 1000km： The Oxford RobotCar Dataset
LiDAR-Video Driving Dataset： Learning Driving Policies Effectively ［
］
KAIST Complex Urban Data Set Dataset ［
dataset
］
Semantic 3D 2017
dataset
Paris-Lille-3D： A Point Cloud Dataset for Urban Scene Segmentation and Classification ［
dataset
］
Semantic KITTI 2019 ［
dataset
］
A*3D： An Autonomous Driving Dataset in Challeging Environments ［
dataset
］，
video
］
HD Map Dataset & Localization Dataset NAVER Labs ： ［
link
］
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
］
Lyft dataset ［
link
］
SemanticPOSS： A Point Cloud Dataset with Large Quantity of Dynamic Instances ［
link
，
］
A2D2 Audi dataset ［
link
］
PandaSet ： Public large-scale dataset for autonomous driving provided by Hesai & Scale。 ［
link
］
Toronto-3D： A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways ［
，
dataset
］
Spatio-Temporal, Movement, Flow estimation in Pointclouds
Rigid Scene Flow for 3D LiDAR Scans IROS 2016 ［
］
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles ［
］
Learning motion field of LiDAR point cloud with convolutional networks ［
link
］
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles ［
video
］
FlowNet3D： Learning Scene Flow in 3D Point Clouds CVPR 2019 ［
，
code
］
LiDAR-Flow： Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images ［
］
4D Spatio-Temporal ConvNets： Minkowski Convolutional Neural Networks CVPR 2019 ［
，
code
］
MeteorNet： Deep Learning on Dynamic 3D Point Cloud Sequences， ICCV 2019 ［
］
DeepLiDARFlow： A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR 2020 ［
］
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
PU-GAN： a Point Cloud Upsampling Adversarial Network ICCV 2019 ［
，
code
］
Neural Architecture Search for Object Detection in Point Cloud ［
blog
］， ［
AutoDeepLabNAS paper
］
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space NeurIPS 2019 ［
］
Domain Adaptation for Vehicle Detection from Bird’s Eye View LiDAR Point Cloud Data ICCVW 2019
Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance ［
］
Quantifying Data Augmentation for LiDAR based 3D Object Detection ［
］
Improving 3D Object Detection through Progressive Population Based Augmentation ［［pdf（
https：//
arxiv。org/abs/2004。0083
1
）］
3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction VEHITS 2020 ［
］
Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution ITSC 2019 ［
］
Performance of LiDAR object detection deep learning architectures based on artificially generated point cloud data from CARLA simulator 2019 ［
］
PointDAN： A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation ［
］
Efficient Learning on Point Clouds with Basis Point Sets ICCV 2019 ［
］
Complete & Label： A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds 2020 ［
］
Neural Implicit Embedding for Point Cloud Analysis CVPR 2020 ［
］
DeepSDF： Learning Continuous Signed Distance Functions for Shape Representation ［
］
Mastering Data Complexity for Autonomous Driving with Adaptive Point Clouds for Urban Environments 2017 ［
］
Visually aided changes detection in 3D lidar based reconstruction 2015 ［
Thesis
］
Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks IROS 2020 ［
，
video
］
Graphs and Pointclouds
Detection of closed sharp edges in point clouds using normal estimation and graph theory CAD 2007 ［
link
］
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs CVPR 2017 ［
，
vide
］
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs CVPR2018 ［
］
ConvPoint： continuous convolutions for cloud processing Eurographics 3DOR， 2019 ［
，
code
］
Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning ［
CVPR Workshop 2019
］，
video
Dynamic Graph CNN for Learning on Point Clouds ［
，
project
］ TOG 2019
Large-scale pointcloud Algorithms (vs scan based)
Deep Parametric Continuous Convolutional Neural Networks CVPR 2018 ［
］
PointNet++： Deep Hierarchical Feature Learning on Point Sets in a Metric Space NeurIPS 2017 ［
，
code
］，
semantic seg code
Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network
Slides
Semantic Segmentation of 3D point Clouds Loic Landireu ［
Slides
］
KPConv： Flexible and Deformable Convolution for Point Clouds ［
，
git
］
RandLA-Net： Efficient Semantic Segmentation of Large-Scale Point Clouds ［
，
git
］
Tools/SW/Packages
Python bindings for Point Cloud Library ［
git
］
Open3D ［
link
］
pyntcloud ［
link
］
PyVista ［
link
］
torch-points3d ： Pytorch framework for doing deep learning on point clouds ［
link
］
Geometric Deep Learning Extension Library for ［PyTorch
link
］
kaolin ： A PyTorch Library for Accelerating 3D Deep Learning Research ［
link
］
PyTorch3D ： FAIR‘s library of reusable components for deep learning with 3D data ［
link
］
PCDet Toolbox in PyTorch for 3D Object Detection from Point Cloud ［
link
］
pointcloudset： Efficient analysis of large datasets of point clouds recorded over time ［
link
］
