Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods

3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

(2020) Inigo Alonso et al.   IEEE Robotics and Automation Letters

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

(2020) Di Feng et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Survey on semantic segmentation using deep learning techniques

(2019) Fahad Lateef et al.   NEUROCOMPUTING

Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis

(2019) Fei Wang et al.   IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

A Review on Deep Learning Techniques for 3D Sensed Data Classification

(2019) David Griffiths et al.   Remote Sensing

A survey on deep learning techniques for image and video semantic segmentation

(2018) Alberto Garcia-Garcia et al.   APPLIED SOFT COMPUTING

Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF

(2018) Huan Luo et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Joint Margin, Cograph, and Label Constraints for Semisupervised Scene Parsing From Point Clouds

(2018) Jie Mei et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification

(2018) Xavier Roynard et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

Methods and datasets on semantic segmentation: A review

(2018) Hongshan Yu et al.   NEUROCOMPUTING

Classification of Urban Point Clouds: A Robust Supervised Approach With Automatically Generating Training Data

(2017) Zhuqiang Li et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Classification of Outdoor 3D Lidar Data Based on Unsupervised Gaussian Mixture Models

(2017) Artur Maligo et al.   IEEE Transactions on Automation Science and Engineering

Deep tracking in the wild: End-to-end tracking using recurrent neural networks

(2017) Julie Dequaire et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

3D computer vision based on machine learning with deep neural networks: A review

(2017) Kailas Vodrahalli et al.   Journal of the Society for Information Display

Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

(2016) Hongyuan Zhu et al.   JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION

TerraMobilita/iQmulus urban point cloud analysis benchmark

(2015) Bruno Vallet et al.   COMPUTERS & GRAPHICS-UK

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

(2015) Martin Weinmann et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

The Pascal Visual Object Classes Challenge: A Retrospective

(2014) Mark Everingham et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Big Data Deep Learning: Challenges and Perspectives

(2014) Xue-Wen Chen et al.   IEEE Access

Toward Open Set Recognition

(2012) W. J. Scheirer et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Contextually guided semantic labeling and search for three-dimensional point clouds

(2012) Abhishek Anand et al.   INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH

3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields

(2009) Ee Hui Lim et al.   COMPUTER-AIDED DESIGN

A practical approach to robotic design for the DARPA Urban Challenge

(2008) Benjamin J. Patz et al.   Journal of Field Robotics