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Fast Segmentation of 3D Point Clouds: A Paradigm on LiDAR Data for Autonomous Vehicle Applications code：https://github.com/VincentCheungM/Run_based_segmentation Time-series LIDAR Data Superimposition for Autonomous Driving Fast segmentation of 3D point clouds for ground vehicles 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 该文章可查看公众号文章 实时稀疏点云分割 CNN for Very Fast Ground Segmentation in Velodyne LiDAR Data A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds Fast Multi-pass 3D Point Segmentation Based on a Structured Mesh Graph for Ground Vehicles Circular Convolutional Neural Networks for Panoramic Images and Laser Data Python bindings for Point Cloud Library code：https://github.com/strawlab/python-pcl  Point Clouds Registration with Probabilistic Data Association code：https://github.com/ethz-asl/robust_point_cloud_registration Robust LIDAR Localization using Multiresolution Gaussian Mixture Maps for Autonomous Driving Automatic Merging of Lidar Point-Clouds Using Data from Low-Cost GPS/IMU Systems 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration Incremental Segment-Based Localization in 3D Point Clouds  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  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 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  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation website:http:///~rqi/pointnet2/ 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 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  Hierarchies of Octrees for Efficient 3D Mapping Adaptive Resolution Grid Mapping using Nd-Tree LIDAR-Data Accumulation Strategy To Generate High Definition Maps For Autonomous Vehicles Long-term 3D map maintenance in dynamic environments Detection and Tracking of Moving Objects Using 2.5D Motion Grids 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 Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data HDNET: Exploiting HD Maps for 3D Object Detection  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  DBSCAN algorithm：A density-based algorithm for discovering clusters in large spatial databases with noise Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection 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  nuScenes : public large-scale dataset for autonomous driving https://www./overview Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification http:///paris-lille-3d Ford Campus Vision and Lidar Data Set http://robots.engin./SoftwareData/Ford Oxford RobotCar dataset 1 Year, 1000km: The Oxford RobotCar Dataset https://robotcar-dataset.robots./ Udacity based simulator https://github.com/EvanWY/USelfDrivingSimulator Tutorial on Gazebo to simulate raycasting from Velodyne lidar http:///tutorials?tut=guided_i1 Udacity Driving Dataset https://github.com/udacity/self-driving-car/tree/master/datasets Virtual KITTI http://www.europe./Research/Computer-Vision/Proxy-Virtual-Worlds KAIST Complex Urban Data Set Dataset http://irap./dataset/download_1.html Semantic KITTI http:/// 希望有跑过以上开源代码的小伙伴能够积极评论与公众号取得联系，分享demo视频，并分享您的想法与更多志同道合的小伙伴一起交流和学习。 公众号将会推送基于PCL库的点云处理，SLAM，三维视觉，高精地图相关的文章。公众号致力于理解三维世界相关内容的干货分享。不仅组织技术交流群，并且组建github组群，可自由分享。交流提问。 历史文章查看点云学习历史文章大汇总 1.一起学SLAM：第三期：一起来学SLAM 2.招募乐于分享的你：点云PCL运营招募啦 3.招募计算机视觉，SLAM，三维视觉，点云等相关领域博客博主，或者公司开设专栏,只要与平台主题相关，乐于分享，都可以与本平台合作经营，发布原创文章。并且可以加入微信，QQ交流群，认识更多志同道合的一起同行分享。 4，相机测评活动：图漾双目，小觅相机，奥比中光三款相机测评活动正在进行中,,,,,, |
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