Intelligent Point Cloud Processing, Sensing and Understanding (Volume II)
Point clouds serve as a fundamental representation of the 3D digital world despite their inherently irregular topologies. Recent advances in sensor technology have significantly improved the acquisition of point cloud data, enabling flexible and scalable geometric representations. These developments...
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| Format: | Online |
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| Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Online Access: | ONIX_20250812T110751_9783725846870_588 |
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| collection | Directory of Open Access Books |
| description | Point clouds serve as a fundamental representation of the 3D digital world despite their inherently irregular topologies. Recent advances in sensor technology have significantly improved the acquisition of point cloud data, enabling flexible and scalable geometric representations. These developments have catalyzed new methodologies and solutions across a wide range of remote sensing applications. State-of-the-art sensors can generate dense point clouds from diverse platforms—including aerial and UAV systems and vehicle-mounted, backpack, handheld, and static terrestrial scanners—capturing objects from multiple perspectives (nadir, oblique, and side views) and spectral bands (e.g., multispectral imaging). Additionally, variations in point density and completeness contribute to different levels of granularity in 3D reconstructions. The applications of point cloud processing continue to expand beyond traditional geospatial analysis, encompassing fields such as manufacturing, civil and mechanical engineering, construction, transportation, ecology, and forestry. This collection of contributions highlights the latest innovations in point cloud registration, compression, and perception, offering insights into existing challenges and paving the way for novel 3D applications. |
| format | Online |
| id | doab-20.500.12854ir-165833 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1658332025-08-12T10:21:10Z Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) Wang, Miaohui Tian, Sukun point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery deep learning for point cloud processing point cloud registration, segmentation, object detection, semantic labelling, compression, and quality assessment modeling of LiDAR/image-based point cloud processing industrial applications with large-scale point clouds high-performance computing for large-scale point clouds thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Point clouds serve as a fundamental representation of the 3D digital world despite their inherently irregular topologies. Recent advances in sensor technology have significantly improved the acquisition of point cloud data, enabling flexible and scalable geometric representations. These developments have catalyzed new methodologies and solutions across a wide range of remote sensing applications. State-of-the-art sensors can generate dense point clouds from diverse platforms—including aerial and UAV systems and vehicle-mounted, backpack, handheld, and static terrestrial scanners—capturing objects from multiple perspectives (nadir, oblique, and side views) and spectral bands (e.g., multispectral imaging). Additionally, variations in point density and completeness contribute to different levels of granularity in 3D reconstructions. The applications of point cloud processing continue to expand beyond traditional geospatial analysis, encompassing fields such as manufacturing, civil and mechanical engineering, construction, transportation, ecology, and forestry. This collection of contributions highlights the latest innovations in point cloud registration, compression, and perception, offering insights into existing challenges and paving the way for novel 3D applications. 2025-08-12T10:21:08Z 2025-08-12T10:21:08Z 2025 book ONIX_20250812T110751_9783725846870_588 9783725846870 9783725846887 https://directory.doabooks.org/handle/20.500.12854/165833 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11247 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4688-7 10.3390/books978-3-7258-4688-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725846870 9783725846887 220 open access |
| spellingShingle | point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery deep learning for point cloud processing point cloud registration, segmentation, object detection, semantic labelling, compression, and quality assessment modeling of LiDAR/image-based point cloud processing industrial applications with large-scale point clouds high-performance computing for large-scale point clouds thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title | Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title_full | Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title_fullStr | Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title_full_unstemmed | Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title_short | Intelligent Point Cloud Processing, Sensing and Understanding (Volume II) |
| title_sort | intelligent point cloud processing sensing and understanding volume ii |
| topic | point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery deep learning for point cloud processing point cloud registration, segmentation, object detection, semantic labelling, compression, and quality assessment modeling of LiDAR/image-based point cloud processing industrial applications with large-scale point clouds high-performance computing for large-scale point clouds thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery deep learning for point cloud processing point cloud registration, segmentation, object detection, semantic labelling, compression, and quality assessment modeling of LiDAR/image-based point cloud processing industrial applications with large-scale point clouds high-performance computing for large-scale point clouds thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20250812T110751_9783725846870_588 |