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

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Lidar Point clound processing for Autonomous Driving

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

LiDAR Pointcloud Clustering/Semantic Segmentation

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 [git]
  • Time-series LIDAR Data Superimposition for Autonomous Driving [pdf]
  • 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 [pdf]
  • A Fast Ground Segmentation Method for 3D Point Cloud [pdf]
  • Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field [pdf]
  • Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA [pdf]
  • Efficient Online Segmentation for Sparse 3D Laser Scans [pdf], [git]
  • CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data [pdf]
  • A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds [pdf]
  • Fast Multi-pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles pdf video
  • Circular Convolutional Neural Networks for Panoramic Images and Laser Data pdf
  • Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds [pdf]
  • Identifying Unknown Instances for Autonomous Driving/Open-set instance segmentation algorithm CoRL 2019 [pdf]
  • RIU-Net: Embarrassingly simple semantic segmentation of3D LiDAR point cloud. [pdf, LU-net]
  • SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving [pdf]

Continous domain DNNs

  • Deep Parametric Continuous Convolutional Neural Networks CVPR 2018 [pdf]

Pointcloud Density

  • DBSCAN : A density-based algorithm for discovering clusters in large spatial databases with noise (1996) [pdf]
  • Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection pdf
  • STD: Sparse-to-Dense 3D Object Detector for Point Cloud pdf
  • Fast semantic segmentation of 3d point clounds with strongly varying density [pdf]
  • The Perfect Match: 3D Point Cloud Matching with Smoothed Densities [pdf, code]

Registration and Localization

  • Point Clouds Registration with Probabilistic Data Association [git]
  • Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving [pdf], [Thesis]
  • Automatic Merging of Lidar Point-Clouds Using Data from Low-Cost GPS/IMU Systems [pdf]
  • 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration [pdf]
  • Incremental Segment-Based Localization in 3D Point Clouds [pdf]

Feature Extraction

  • Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR [pdf]
  • Finding Planes in LiDAR Point Clouds for Real-Time Registration [pdf]
  • Online detection of planes in 2D lidar [pdf]
  • A Fast RANSAC–Based Registration Algorithm for Accurate Localization in Unknown Environments using LIDAR Measurements [pdf]
  • Hierarchical Plane Extraction (HPE): An Efficient Method For Extraction Of Planes From Large Pointcloud Datasets [pdf]
  • A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing [pdf]
  • SPLATNet: Sparse Lattice Networks for Point Cloud Processing CVPR 2018 [pdf, code]

Object detection and Tracking

  • Learning a Real-Time 3D Point Cloud Obstacle Discriminator via Bootstrapping pdf
  • Terrain-Adaptive Obstacle Detection [pdf]
  • 3D Object Detection from Roadside Data Using Laser Scanners [pdf]
  • 3D Multiobject Tracking for Autonomous Driving : Masters thesis A S Abdul Rahman
  • Motion-based Detection and Tracking in 3D LiDAR Scans [pdf]
  • Lidar-histogram for fast road and obstacle detection [pdf]
  • End-to-end Learning of Multi-sensor 3D Tracking by Detection pdf
  • Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection pdf
  • Deep tracking in the wild: End-to-end tracking using recurrent neural networks [pdf]
  • Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf], [video]
  • VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection CVPR 2018 [pdf, code]
  • PIXOR: Real-time 3D Object Detection from Point Clouds CVPR 2018 [pdf]
  • Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges [pdf]
  • Low resolution lidar-based multi-object tracking for driving applications pdf
  • Patch Refinement -- Localized 3D Object Detection pdf

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 pdf
  • Improving LiDAR Point Cloud Classification using Intensities and Multiple Echoes [pdf]
  • DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [pdf]
  • 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 [pdf]
  • PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud [pdf]
  • Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks [pdf]
  • ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA [pdf]

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

  • Hierarchies of Octrees for Efficient 3D Mapping pdf
  • Adaptive Resolution Grid Mapping using Nd-Tree [ieee], [pdf], [video]
  • LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles [link]
  • Long-term 3D map maintenance in dynamic environments [video]
  • Detection and Tracking of Moving Objects Using 2.5D Motion Grids [pdf]
  • 3D Lidar-based Static and Moving Obstacle Detection in Driving Environments: an approach based on voxels and multi-region ground planes [pdf]
  • 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]
  • Fast 3-D Urban Object Detection on Streaming Point Clouds [pdf]
  • Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review [pdf]
  • Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping [pdf]
  • DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map [pdf],[video]
  • Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data [pdf]
  • HDNET: Exploiting HD Maps for 3D Object Detection [pdf]

End-To-End Learning

  • Monocular Fisheye Camera Depth Estimation Using Semi-supervised Sparse Velodyne Data [pdf]
  • Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [pdf]

Lidar Datasets and Simulators

  • nuScenes : public large-scale dataset for autonomous driving [dataset]
  • A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving [pdf]
  • Ford Campus Vision and Lidar Data Set [pdf, dataset]
  • Oxford RobotCar dataset dataset 1 Year, 1000km: The Oxford RobotCar Dataset pdf
  • Udacity based simulator [link, git]
  • Tutorial on Gazebo to simulate raycasting from Velodyne lidar [link]
  • Udacity Driving Dataset [link]
  • Virtual KITTI [link]
  • LiDAR-Video Driving Dataset: Learning Driving Policies Effectively [pdf]
  • KAIST Complex Urban Data Set Dataset [dataset]
  • Semantic KITTI [dataset]
  • A*3D: An Autonomous Driving Dataset in Challeging Environments [dataset], [video]

Spatio-Temporal, Movement, Flow estimation in Pointclouds

  • Rigid Scene Flow for 3D LiDAR Scans IROS 2016 [pdf]
  • Deep Lidar CNN to Understand the Dynamics of Moving Vehicles [pdf]
  • Learning motion field of LiDAR point cloud with convolutional networks [link]
  • Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles [pdf video]
  • FlowNet3D: Learning Scene Flow in 3D Point Clouds CVPR 2019 [pdf, code]
  • LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Images [pdf]
  • 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks CVPR 2019 [pdf, code]
  • MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences, ICCV 2019 [pdf]

Advanced Topics/Other applications

TAsks : Upsampling, Domain adaptation Sim2Real, NAS, SSL

  • PU-GAN: a Point Cloud Upsampling Adversarial Network ICCV 2019 [pdf, 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 [pdf]
  • Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data ICCVW 2019 pdf
  • Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance [pdf]

Large-scale pointcloud Algorithms (vs scan based)

  • Datasets :
    • Semantic 3D dataset
    • Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification [pdf dataset]
    • SynthCity: A large-scale synthetic point cloud 2019 [dataset, pdf]
    • HD Map Dataset & Localization Dataset NAVER Labs : [link]
  • Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs CVPR2018 [pdf]
  • PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space NeurIPS 2017 [pdf, code], semantic seg code
  • ConvPoint: continuous convolutions for cloud processing Eurographics 3DOR, 2019 pdf, code
  • Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network Slides
  • Semantic Segmentation of 3D point Clouds Loic Landireu [Slides]
  • Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning [CVPR Workshop 2019], video

Tools/SW/Packages

  • Python bindings for Point Cloud Library [git]
  • Open3D [link]
  • pyntcloud [link]
  • PyVista [link]

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