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Keyword

point cloud classification

Description: The task of assigning semantic categories to points or regions in three-dimensional point cloud data, often using machine learning.
OpenAlex ID: keywords/point-cloud-classification
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
2017 · Qi, Charles R., Yi, Li, et al. · arXiv (Cornell University) · 9,447
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Transformers in Vision: A Survey
2022 · Salman Khan, Muzammal Naseer, et al. · ACM Computing Surveys · 3,580
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KPConv: Flexible and Deformable Convolution for Point Clouds
2019 · Hugues Thomas, Charles R. Qi, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 3,386
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Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
2017 · Martin Simonovsky, Nikos Komodakis · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 1,336
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A progressive morphological filter for removing nonground measurements from airborne LIDAR data
2003 · Keqi Zhang, Shu-Ching Chen, et al. · IEEE Transactions on Geoscience and Remote Sensing · 1,030
SO-Net: Self-Organizing Network for Point Cloud Analysis
2018 · Jiaxin Li, Ben M. Chen, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 1,025
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Relation-Shape Convolutional Neural Network for Point Cloud Analysis
2019 · Yongcheng Liu, Bin Fan, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 1,024
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
2019 · Hengshuang Zhao, Li Jiang, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 891
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
2019 · Mikaela Angelina Uy, Quang-Hieu Pham, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 885
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Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
2022 · Xumin Yu, Lulu Tang, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 808
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PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling
2020 · Xu Yan, Chaoda Zheng, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 713
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Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
2015 · Martin Weinmann, Boris Jutzi, et al. · ISPRS Journal of Photogrammetry and Remote Sensing · 711
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
2020 · Ying Li, Lingfei Ma, et al. · IEEE Transactions on Neural Networks and Learning Systems · 614
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Contextual classification of lidar data and building object detection in urban areas
2013 · Joachim Niemeyer, Franz Rottensteiner, et al. · ISPRS Journal of Photogrammetry and Remote Sensing · 577
Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues
2010 · Xuelian Meng, Nate Currit, et al. · Remote Sensing · 575
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PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
2021 · Mutian Xu, Runyu Ding, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 555
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Point convolutional neural networks by extension operators
2018 · Matan Atzmon, Haggai Maron, et al. · ACM Transactions on Graphics · 546
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PointCLIP: Point Cloud Understanding by CLIP
2022 · Renrui Zhang, Ziyu Guo, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 471
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Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
2022 · Ma, Xu, Qin, Can, et al. · arXiv (Cornell University) · 447
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A Review of Point Clouds Segmentation and Classification Algorithms
2017 · E. Grilli, F. Menna, et al. · ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 445
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3D point cloud segmentation: A survey
2013 · Anh Nguyen, Bac Le · IEEE Conference on Robotics, Automation and Mechatronics (RAM) · 429
Perceiver: General Perception with Iterative Attention
2021 · Jaegle, Andrew, Gimeno, Felix, et al. · arXiv (Cornell University) · 414
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Deep Sets
2017 · Manzil Zaheer, Satwik Kottur, et al. · arXiv (Cornell University) · 406
Attentional ShapeContextNet for Point Cloud Recognition
2018 · Saining Xie, Sainan Liu, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 356
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
2005 · D. Anguelov, B. Taskar, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 356
Works count: 4,046
Citation count: 76,490
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