Zobrazeno 1 - 10
of 712
pro vyhledávání: '"point cloud {classification}"'
Publikováno v:
Alexandria Engineering Journal, Vol 111, Iss , Pp 530-539 (2025)
Recent autonomous driving systems heavily rely on 3D point cloud data collected from multiple sensors for environmental awareness and decision-making. However, it is unrealistic to expect the autonomous driving system to recognize all road environmen
Externí odkaz:
https://doaj.org/article/8998ecbef982478ba162bf5a4ff440d7
Autor:
Shuo Shi, Biwu Chen, Sifu Bi, Junkai Li, Wei Gong, Jia Sun, Bowen Chen, Lin Du, Jian Yang, Qian Xu, Fei Wang, Shalei Song
Publikováno v:
Geo-spatial Information Science, Vol 27, Iss 5, Pp 1460-1474 (2024)
Precise classification of Light Detection and Ranging (LiDAR) point cloud is a fundamental process in various applications, such as land cover mapping, forestry management, and autonomous driving. Due to the lack of spectral information, the existing
Externí odkaz:
https://doaj.org/article/ce02f9c0e2c74cc286a61707e803688b
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2024, Iss 1, Pp 1-15 (2024)
Abstract 3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and p
Externí odkaz:
https://doaj.org/article/5c951159e03442149eb65d57b4fa4126
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTThe urban environment exhibits significant vertical variations, Light Detection and Ranging (LiDAR) point cloud classification can provide insights for the 3D morphology of the urban environment. Introducing the adjacency relationships betwee
Externí odkaz:
https://doaj.org/article/3973ad9126bc49688dbe11b969d7a6e3
Akademický článek
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Publikováno v:
In Computers & Graphics December 2024 125
Autor:
Xiang Huang, Feng Cheng, Yinli Bao, Cheng Wang, Jinliang Wang, Junen Wu, Junliang He, Jieying Lao
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103870- (2024)
Although the photon point cloud data acquired from ICESat-2/ATLAS can be efficiently employed in urban building height extraction, its universal applicability in undulating terrain scenarios is constrained, and there are noticeable issues of false po
Externí odkaz:
https://doaj.org/article/ad2e6217587d447ca5600bfe63a0943e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5637-5650 (2024)
Multispectral point cloud, with spatial and multiple-band spectral information, provides the data basis for finer land cover 3-D classification. However, spectral information is not well utilized by traditional methods of point cloud classification.
Externí odkaz:
https://doaj.org/article/6312ab6111ff4968ae4f9861eac9cdac
Autor:
Simone Ott, Benjamin Burkhard, Corinna Harmening, Jens-André Paffenholz, Bastian Steinhoff-Knopp
Publikováno v:
Geomatics, Vol 3, Iss 4, Pp 501-521 (2023)
Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly ba
Externí odkaz:
https://doaj.org/article/8e20a473b26e4ada935d593c19652546
Publikováno v:
Electronic Research Archive, Vol 31, Iss 12, Pp 7365-7384 (2023)
Among all usual formats of representing 3D objects, including depth image, mesh and volumetric grid, point cloud is the most commonly used and preferred format, because it preserves the original geometric information in 3D space without any discretiz
Externí odkaz:
https://doaj.org/article/dcc979ceebe0469e9b6848e5c15ef3e8