Intelligent Point Cloud Processing, Sensing and Understanding
Point clouds are deemed to be one of the foundational pillars in representing the 3D digital world, despite irregular topologies among discrete points. Recently, advancements in sensor technologies that acquire point cloud data for flexible and scalable geometric representation have paved the way fo...
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| Format: | Online |
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| Jezik: | angleščina |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Online dostop: | ONIX_20240514_9783725802418_207 |
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| collection | Directory of Open Access Books |
| description | Point clouds are deemed to be one of the foundational pillars in representing the 3D digital world, despite irregular topologies among discrete points. Recently, advancements in sensor technologies that acquire point cloud data for flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications. State-of-the-art sensors are capable of capturing and describing objects in a scene by using dense point clouds from various platforms (satellites, aerial, UAVs, vehicle-borne, backpacks, handheld, and static terrestrial), perspectives (nadir, oblique, and side view), spectra (multispectral), and granularity (point density and completeness). Meanwhile, the ever-expanding application areas of point cloud processing have already covered not only conventional domains in geospatial analysis but also manufacturing, civil engineering, construction, transportation, ecology, forestry, mechanical engineering, etc. Readers can learn about the latest innovative technologies for generating, processing, and analyzing point cloud data from these contributions, which helps to understand the challenges faced by point cloud data and develop new 3D applications. |
| format | Online |
| id | doab-20.500.12854ir-137608 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1376082024-05-14T13:43:31Z Intelligent Point Cloud Processing, Sensing and Understanding Wang, Miaohui Yue, Guanghui Xiong, Jian 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 fusion of multimodal point clouds 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 Point clouds are deemed to be one of the foundational pillars in representing the 3D digital world, despite irregular topologies among discrete points. Recently, advancements in sensor technologies that acquire point cloud data for flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications. State-of-the-art sensors are capable of capturing and describing objects in a scene by using dense point clouds from various platforms (satellites, aerial, UAVs, vehicle-borne, backpacks, handheld, and static terrestrial), perspectives (nadir, oblique, and side view), spectra (multispectral), and granularity (point density and completeness). Meanwhile, the ever-expanding application areas of point cloud processing have already covered not only conventional domains in geospatial analysis but also manufacturing, civil engineering, construction, transportation, ecology, forestry, mechanical engineering, etc. Readers can learn about the latest innovative technologies for generating, processing, and analyzing point cloud data from these contributions, which helps to understand the challenges faced by point cloud data and develop new 3D applications. 2024-05-14T13:43:22Z 2024-05-14T13:43:22Z 2024 book ONIX_20240514_9783725802418_207 9783725802418 9783725802425 https://directory.doabooks.org/handle/20.500.12854/137608 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8793 https://mdpi.com/books/pdfview/book/8793 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0242-5 10.3390/books978-3-7258-0242-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725802418 9783725802425 208 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 fusion of multimodal point clouds 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 Intelligent Point Cloud Processing, Sensing and Understanding |
| title | Intelligent Point Cloud Processing, Sensing and Understanding |
| title_full | Intelligent Point Cloud Processing, Sensing and Understanding |
| title_fullStr | Intelligent Point Cloud Processing, Sensing and Understanding |
| title_full_unstemmed | Intelligent Point Cloud Processing, Sensing and Understanding |
| title_short | Intelligent Point Cloud Processing, Sensing and Understanding |
| title_sort | intelligent point cloud processing sensing and understanding |
| 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 fusion of multimodal point clouds 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 |
| 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 fusion of multimodal point clouds 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 |
| url | ONIX_20240514_9783725802418_207 |