Literatura académica sobre el tema "Point cloud analysis"
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Artículos de revistas sobre el tema "Point cloud analysis"
Pu, Xinming, Shu Gan, Xiping Yuan y Raobo Li. "Feature Analysis of Scanning Point Cloud of Structure and Research on Hole Repair Technology Considering Space-Ground Multi-Source 3D Data Acquisition". Sensors 22, n.º 24 (8 de diciembre de 2022): 9627. http://dx.doi.org/10.3390/s22249627.
Texto completoCai, S., W. Zhang, J. Qi, P. Wan, J. Shao y A. Shen. "APPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 de abril de 2018): 107–11. http://dx.doi.org/10.5194/isprs-archives-xlii-3-107-2018.
Texto completoAlsadik, B., M. Gerke y G. Vosselman. "Visibility analysis of point cloud in close range photogrammetry". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-5 (28 de mayo de 2014): 9–16. http://dx.doi.org/10.5194/isprsannals-ii-5-9-2014.
Texto completoLiu, Chang, Haiyun Gan, Jialin Li y Boqing Zhu. "Rasterize the Lidar Point Cloud on The Ground Out Method Optimization Analysis". Journal of Physics: Conference Series 2405, n.º 1 (1 de diciembre de 2022): 012005. http://dx.doi.org/10.1088/1742-6596/2405/1/012005.
Texto completoWu, Youping y Zhihui Zhou. "Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm". Journal of Function Spaces 2022 (21 de julio de 2022): 1–10. http://dx.doi.org/10.1155/2022/6135829.
Texto completoYu, Ruixuan y Jian Sun. "Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis". Sensors 21, n.º 12 (19 de junio de 2021): 4211. http://dx.doi.org/10.3390/s21124211.
Texto completoZhang, Yan, Wenhan Zhao, Bo Sun, Ying Zhang y Wen Wen. "Point Cloud Upsampling Algorithm: A Systematic Review". Algorithms 15, n.º 4 (8 de abril de 2022): 124. http://dx.doi.org/10.3390/a15040124.
Texto completoPan, Liang, Pengfei Wang y Chee-Meng Chew. "PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis". IEEE Robotics and Automation Letters 4, n.º 4 (octubre de 2019): 4035–41. http://dx.doi.org/10.1109/lra.2019.2927948.
Texto completoAhmad, N., S. Azri, U. Ujang, M. G. Cuétara, G. M. Retortillo y S. Mohd Salleh. "COMPARATIVE ANALYSIS OF VARIOUS CAMERA INPUT FOR VIDEOGRAMMETRY". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (1 de octubre de 2019): 63–70. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-63-2019.
Texto completoXu, Y., Z. Sun, R. Boerner, T. Koch, L. Hoegner y U. Stilla. "GENERATION OF GROUND TRUTH DATASETS FOR THE ANALYSIS OF 3D POINT CLOUDS IN URBAN SCENES ACQUIRED VIA DIFFERENT SENSORS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (30 de abril de 2018): 2009–15. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2009-2018.
Texto completoTesis sobre el tema "Point cloud analysis"
Forsman, Mona. "Point cloud densification". Thesis, Umeå universitet, Institutionen för fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39980.
Texto completoDonner, Marc, Sebastian Varga y Ralf Donner. "Point cloud generation for hyperspectral ore analysis". Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-231365.
Texto completoDonner, Marc, Sebastian Varga y Ralf Donner. "Point cloud generation for hyperspectral ore analysis". TU Bergakademie Freiberg, 2017. https://tubaf.qucosa.de/id/qucosa%3A23196.
Texto completoAwadallah, Mahmoud Sobhy Tawfeek. "Image Analysis Techniques for LiDAR Point Cloud Segmentation and Surface Estimation". Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73055.
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Burwell, Claire Leonora. "The effect of 2D vs. 3D visualisation on lidar point cloud analysis tasks". Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37950.
Texto completoBungula, Wako Tasisa. "Bi-filtration and stability of TDA mapper for point cloud data". Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6918.
Texto completoMegahed, Fadel M. "The Use of Image and Point Cloud Data in Statistical Process Control". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/26511.
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Chleborad, Aaron A. "Grasping unknown novel objects from single view using octant analysis". Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4089.
Texto completoRasmussen, Johan y David Nilsson. "Analys av punktmoln i tre dimensioner". Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-36915.
Texto completoPurpose: To develop a method that can help smaller sawmills to better utilize the greatest possible amount of wood from a log. Method: A quantitative study where three iterations has been made using Design Science. Findings: To create an effective algorithm that will perform volume calculations in a point cloud consisting of about two million points for an industrial purpose, the focus is on the algorithm being fast and that it shows the correct data. The primary goal of making the algorithm quick is to process the point cloud a minimum number of times. The algorithm that meets the goals in this study is Algorithm C. The algorithm is both fast and has a low standard deviation of the measurement errors. Algorithm C has the complexity O(n) in the analysis of sub-point clouds. Implications: Based on this study’s algorithm, it would be possible to use stereo camera technology to help smaller sawmills to better utilize the most possible amount of wood from a log. Limitations: The study’s algorithm assumes that no points have been created inside the log, which could lead to misplaced points. If a log would be crooked, the center of the log would not match the z-axis position. This is something that could mean that the z-value is outside of the log, in extreme cases, which the algorithm cannot handle.
Rusinek, Cory A. "New Avenues in Electrochemical Systems and Analysis". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1490350904669695.
Texto completoLibros sobre el tema "Point cloud analysis"
Liu, Shan, Min Zhang, Pranav Kadam y C. C. Jay Kuo. 3D Point Cloud Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0.
Texto completo3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2021.
Buscar texto completo3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer International Publishing AG, 2022.
Buscar texto completoKleinman, Daniel Lee, Karen A. Cloud-Hansen y Jo Handelsman, eds. Controversies in Science and Technology. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199383771.001.0001.
Texto completoBurford, Mark. Mahalia Jackson and the Black Gospel Field. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190634902.001.0001.
Texto completoCapítulos de libros sobre el tema "Point cloud analysis"
Weinmann, Martin. "Point Cloud Registration". En Reconstruction and Analysis of 3D Scenes, 55–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29246-5_4.
Texto completoLiu, Shan, Min Zhang, Pranav Kadam y C. C. Jay Kuo. "Deep Learning-Based Point Cloud Analysis". En 3D Point Cloud Analysis, 53–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_3.
Texto completoLiu, Shan, Min Zhang, Pranav Kadam y C. C. Jay Kuo. "Introduction". En 3D Point Cloud Analysis, 1–13. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_1.
Texto completoLiu, Shan, Min Zhang, Pranav Kadam y C. C. Jay Kuo. "Conclusion and Future Work". En 3D Point Cloud Analysis, 141–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_5.
Texto completoLiu, Shan, Min Zhang, Pranav Kadam y C. C. Jay Kuo. "Explainable Machine Learning Methods for Point Cloud Analysis". En 3D Point Cloud Analysis, 87–140. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89180-0_4.
Texto completoMémoli, Facundo y Guillermo Sapiro. "Computing with Point Cloud Data". En Statistics and Analysis of Shapes, 201–29. Boston, MA: Birkhäuser Boston, 2006. http://dx.doi.org/10.1007/0-8176-4481-4_8.
Texto completoWeinmann, Martin. "Preliminaries of 3D Point Cloud Processing". En Reconstruction and Analysis of 3D Scenes, 17–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29246-5_2.
Texto completoKyöstilä, Tomi, Daniel Herrera C., Juho Kannala y Janne Heikkilä. "Merging Overlapping Depth Maps into a Nonredundant Point Cloud". En Image Analysis, 567–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38886-6_53.
Texto completoRichtsfeld, Mario y Markus Vincze. "Point Cloud Segmentation Based on Radial Reflection". En Computer Analysis of Images and Patterns, 955–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_116.
Texto completoSalih, Yasir, Aamir Saeed Malik, Nicolas Walter, Désiré Sidibé, Naufal Saad y Fabrice Meriaudeau. "Noise Robustness Analysis of Point Cloud Descriptors". En Advanced Concepts for Intelligent Vision Systems, 68–79. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02895-8_7.
Texto completoActas de conferencias sobre el tema "Point cloud analysis"
Liu, Yichen. "Point Cloud registration based on iterative closest point". En 2021 International Conference on Computer Vision and Pattern Analysis, editado por Ruimin Hu, Yang Yue y Siting Chen. SPIE, 2022. http://dx.doi.org/10.1117/12.2626850.
Texto completoChen, Haiwei, Shichen Liu, Weikai Chen, Hao Li y Randall Hill. "Equivariant Point Network for 3D Point Cloud Analysis". En 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01428.
Texto completoLu, Jia y Jing Qian. "Discrete Stress Analysis on Point-Cloud Model Derived From Medical Images". En ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206209.
Texto completoDargahi, Mozhgan Momtaz y David Lattanzi. "Spatial Statistical Methods for Complexity-Based Point Cloud Analysis". En ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2294.
Texto completoKadam, Pranav, Min Zhang, Shan Liu y C. C. Jay Kuo. "Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)". En 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2020. http://dx.doi.org/10.1109/vcip49819.2020.9301874.
Texto completoSrivatsan, Vijay y Reuven Katz. "In-Process Surface Normal Estimation for Raster Scanned Point Cloud Data". En ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59007.
Texto completoLi, Pei-Heng, Juo-Wei Lin, Yi-Lun Huang y Ting-Lan Lin. "Analysis of Octree Coding for 3D Point Cloud Frame". En ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97328.
Texto completoLiu, Fayao, Guosheng Lin, Chuan-Sheng Foo, Chaitanya K. Joshi y Jie Lin. "Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis". En 2022 International Conference on 3D Vision (3DV). IEEE, 2022. http://dx.doi.org/10.1109/3dv57658.2022.00017.
Texto completoWei, Shuangfeng y Hong Chen. "Building depth images from scattered point cloud". En International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, editado por Yaolin Liu y Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838404.
Texto completoFujiwara, Kent y Taiichi Hashimoto. "Neural Implicit Embedding for Point Cloud Analysis". En 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01175.
Texto completoInformes sobre el tema "Point cloud analysis"
Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman y John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), abril de 2021. http://dx.doi.org/10.21079/11681/40401.
Texto completoBerney, Ernest, Andrew Ward y Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), marzo de 2021. http://dx.doi.org/10.21079/11681/40042.
Texto completoKholoshyn, Ihor V., Olga V. Bondarenko, Olena V. Hanchuk y Iryna M. Varfolomyeyeva. Cloud technologies as a tool of creating Earth Remote Sensing educational resources. [б. в.], julio de 2020. http://dx.doi.org/10.31812/123456789/3885.
Texto completoMarienko, Maiia V., Yulia H. Nosenko y Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], noviembre de 2020. http://dx.doi.org/10.31812/123456789/4418.
Texto completoHabib, Ayman, Darcy M. Bullock, Yi-Chun Lin y Raja Manish. Road Ditch Line Mapping with Mobile LiDAR. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317354.
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