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[1] Zheng Chen,et al. A Review of Deep Learning-Based Semantic Segmentation for Point Cloud , 2019, IEEE Access.
[2] Touradj Ebrahimi,et al. PointXR: A Toolbox for Visualization and Subjective Evaluation of Point Clouds in Virtual Reality , 2020, 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX).
[3] P. Song,et al. China has faster pace than Japan in population aging in next 25 years. , 2019, Bioscience trends.
[4] Zhen Wang,et al. A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[5] Antonios Tsourdos,et al. Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions , 2019, ArXiv.
[6] Siheng Chen,et al. 3D Point Cloud Processing and Learning for Autonomous Driving , 2020, ArXiv.
[7] Alberto L. Sangiovanni-Vincentelli,et al. A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving , 2018, ICMR.
[8] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[9] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[10] Touradj Ebrahimi,et al. Towards subjective quality assessment of point cloud imaging in augmented reality , 2017, 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).
[11] Kevin Ponto,et al. Progressive feedback point cloud rendering for virtual reality display , 2016, 2016 IEEE Virtual Reality (VR).
[12] Xiao Xiang Zhu,et al. A Review of Point Cloud Semantic Segmentation , 2019, ArXiv.
[13] P. Song,et al. Internal migration and regional differences of population aging: An empirical study of 287 cities in China. , 2018, Bioscience trends.
[14] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.
[15] Silvio Savarese,et al. 3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Lars Petersson,et al. Non-associative Higher-Order Markov Networks for Point Cloud Classification , 2014, ECCV.
[17] C. Mallet,et al. AIRBORNE LIDAR FEATURE SELECTION FOR URBAN CLASSIFICATION USING RANDOM FORESTS , 2009 .
[18] J. Niemeyer,et al. Contextual classification of lidar data and building object detection in urban areas , 2014 .
[19] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Ling Chen,et al. Few-Shot Learning for Remote Sensing Image Retrieval With MAML , 2020, 2020 IEEE International Conference on Image Processing (ICIP).
[21] Subhransu Maji,et al. Multi-view Convolutional Neural Networks for 3D Shape Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[22] Large-Scale 3D Point Cloud Processing for Mixed and Augmented Reality , 2018, 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct).
[23] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[24] Bo Du,et al. A Three-Step Approach for TLS Point Cloud Classification , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[25] Xiangguo Lin,et al. SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas , 2013, Remote. Sens..
[26] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Sander Oude Elberink,et al. Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.
[28] Yoshua Bengio,et al. MetaGAN: An Adversarial Approach to Few-Shot Learning , 2018, NeurIPS.