README CVPR2019 Point Cloud

README 2016-CHANGE DETECTION OF MOBILE LIDAR DATA USING CLOUD COMPUTING

README 2015-Octree-based region growing for point cloud segmentation

README 2018-PIXOR: Real-time 3D Object Detection from Point Clouds 2008-LIDAR-based 3D Object Perception 2017-Object Detection and Classification in 3D Point Cloud Data for Automated Driving

README 2007-Simultaneous Localization, Mapping and Moving Object Tracking(0%) 2008-Model Based Vehicle Tracking for Autonomous Driving in Urban Environments(50%) 2010-Tracking People with a 360-Degree Lidar (70%) 2011-Tracking People in 3D Using a Bottom-Up Top-Down Detector 2010-The probabilistic data association filter 2014-Effective Data Association Algorithms for Multi target Tracking [추천] 2012-LIDAR-BASED MULTI-OBJECT TRACKING SYSTEM WITH DYNAMIC MODELING 2014-Confidence-Based Pedestrian Tracking in Unstructured Environments Using 3D Laser Distance Measurements 2014-다중표적 추적필터와 자료연관 기법 동향 2015-Person Tracking and Following with 2D Laser Scanners 2015-자율 주행 차량의 다중 물체 추적 알고리즘 2016-[SORT]Simple online and realtime tracking 2017-Detect to Track and Track to Detect 2016-Lidar-based Methods for Tracking and Identification [추천] 2017-pedestrian tracking using velodyne data -stochastic optimization for extended tracking 2017-Low resolution lidar-based multi-object tracking for driving application [추천] 2017-3D-LIDAR Multi Object Tracking for Autonomous Driving 2017-Online learning for human classification in 3D LiDAR-based tracking 2018-Tracklet-Association-Tracker(30%) 2019-Self-Driving Cars: A Survey 2019-Data Association for Multi-Object Tracking via Deep Neural Networks 2019-A portable three-dimensional LIDARbased system for long-term and widearea people behavior measurement

README.md 2017-3D Bounding Box Estimation Using Deep Learning and Geometry 2017-Vehicle Detection and Pose Estimation in Autonomous Convoys

2012-Car2X-based perception in a high-level fusion architecture for cooperative perception systems (30%) 2013-Inter-Vehicle Object Association for Cooperative Perception Systems (10%) 2017-Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles (5%) 2017-Track-to-track fusion multi-target tracking using asynchronous and delayed data

README.md 2018-Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net

2008-단일 카메라를 이용한 보행자의 높이 및 위치 추정 기법 2010-Pedestrian Detection and Tracking Using Three-dimensional LADAR Data 2011-Pedestrian Recognition Using High-definition LIDAR 2012-Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite 2014-Deep Learning Representation using Autoencoder for 3D Shape Retrieval 2014-Know Your Limits: Accuracy of Long Range Stereoscopic Object Measurements in Practice 2014-Frame Rate Fusion and Upsampling of EO/LIDAR Data for Multiple Platforms 2014-2D/3D Sensor Exploitation and Fusion for Enhanced Object Detection 2014-단일 카메라 영상에서의 보행자 거리 추정 2015-단일 카메라를 이용한 3차원 공간 정보 생성 2015-3D Deep Shape Descriptor 2015-Deep Learning Representation using Autoencoder for 3D Shape Retrieval 2015-3D Mesh Labeling via Deep Convolutional Neural Networks 2015-Multi-view Convolutional Neural Networks for 3D Shape Recognition 2015-DeepPano: Deep Panoramic Representation for 3-D Shape Recognition 2015-3D ShapeNets: A Deep Representation for Volumetric Shapes 2015-VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition 2015-Depth Map Prediction from a Single Image using a Multi-Scale Deep Network 2015-Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields 2015-FlowNet: Learning Optical Flow with Convolutional Networks 2015-PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung 2016-Volumetric and Multi-View CNNs for Object Classification on 3D Data 2016-Pairwise Decomposition of Image Sequences for Active Multi-View Recognition 2016-Vehicle Detection from 3D Lidar Using Fully Convolutional Network 2016-Accelerated Generative Models for 3D Point Cloud Data 2016-FPNN: Field Probing Neural Networks for 3D Data 2016-Multi-Sensor Fusion of Occupancy Grids based on Integer Arithmetic 2016-PointNet: A 3D Convolutional Neural Network for real-time object class recognition 2016-3D Fully Convolutional Network for Vehicle Detection in Point Cloud 2016-Monocular 3D Object Detection for Autonomous Driving 2016-Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs? 2016-3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction 2016-단일 카메라를 이용한 차량 검출 및 거리추정 2016-A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation 2016-Efficient Deep Learning for Stereo Matching 2016-Deep Stereo Fusion: combining multiple disparity hypotheses with deep-learning 2017-Unsupervised Monocular Depth Estimation with Left-Right Consistency 2017-Toward Domain Independence for Learning-Based Monocular Depth Estimation 2017-Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference 2017-J-MOD^2: Joint Monocular Obstacle Detection and Depth Estimation 2017-End-to-end deep stereo regression architecture 2017-3D Object Proposals using Stereo Imagery for Accurate Object Class Detection 2017-OctNet: Learning Deep 3D Representations at High Resolutions 2017-Depth Estimation from Single Image Using CNN-Residual Network 2017-PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation 2017-Efficient Online Segmentation for Sparse 3D Laser Scans 2017-Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks 2017-Multi-View 3D Object Detection Network for Autonomous Driving 2017-SEGCloud: Semantic Segmentation of 3D Point Clouds 2017-VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection 2017-Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks 2017-SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud 2018-SECOND:Sparsely Embedded Convolutional Detection 2018-PU-Net: Point Cloud Upsampling Network 2019-Pseudo-LiDAR from Visual Depth Estimation 2019-Stereo R-CNN based 3D Object Detection for Autonomous Driving