Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
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Title
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Authors
Keywords
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Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 7, Pages 6063-6081
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2021-05-13
DOI
10.1109/tits.2021.3076844
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Note: Only part of the references are listed.- 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
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