Zobrazeno 1 - 10
of 1 070
pro vyhledávání: '"point cloud segmentation"'
Publikováno v:
智慧农业, Vol 6, Iss 4, Pp 64-75 (2024)
ObjectiveThe body size parameter of cattle is a key indicator reflecting the physical development of cattle, and is also a key factor in the cattle selection and breeding process. In order to solve the demand of measuring body size of beef cattle in
Externí odkaz:
https://doaj.org/article/dbc05e21dfd342f2b48a5ef3b0a28e8a
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 132, Iss , Pp 104020- (2024)
Three-dimensional laser scanning technology is widely employed in various fields due to its advantage in rapid acquisition of geographic scene structures. Achieving high precision and automated semantic segmentation of three-dimensional point cloud d
Externí odkaz:
https://doaj.org/article/79c305eb97224cebbcdba24b2d221649
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 9, Iss 2, Pp 348-364 (2024)
Abstract Non‐destructive detection of wire bonding defects in integrated circuits (IC) is critical for ensuring product quality after packaging. Image‐processing‐based methods do not provide a detailed evaluation of the three‐dimensional defe
Externí odkaz:
https://doaj.org/article/ea87c73fdd7549a4bf1c4dccc94a4704
Publikováno v:
AgriEngineering, Vol 6, Iss 1, Pp 539-554 (2024)
The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct
Externí odkaz:
https://doaj.org/article/669149ba95614888b7d31d22d8629ebf
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 7, Pp 98-106, 178 (2024)
Due to the influence of underground coal dust and easy obstruction, the laser point cloud data of hydraulic supports is prone to be incomplete. The existing point cloud segmentation algorithms are difficult to obtain fine-grained point cloud features
Externí odkaz:
https://doaj.org/article/b934cefeb477482ca988e3c2852ed14f
Autor:
Xiaoyan Zhang, Lin Feng
Publikováno v:
IEEE Access, Vol 12, Pp 70550-70558 (2024)
Aiming at the problem of poor edge effect segmentation in point cloud segmentation, which fails to fully utilize the correlation between the local geometric and semantic features of point cloud.We propose an edge-enhanced graph convolution point clou
Externí odkaz:
https://doaj.org/article/4a8545836e92413fb6eac2306bd1ff6e
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 855-870 (2024)
Multispectral LiDAR can rapidly acquire 3D and spectral information of objects, providing richer features for point cloud semantic segmentation. Despite the remarkable performance of existing graph neural networks in point cloud segmentation, extract
Externí odkaz:
https://doaj.org/article/c3fc6ed8b9d8407c869203220759f2d9
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 18, Iss 6, Pp 268-274 (2023)
ObjectivesLaser scanning technology used in the intelligent installation of ship shafting has such advantages as non-contact, high-speed scanning and high-precision imaging. The laser point cloud data includes the size, position and direction informa
Externí odkaz:
https://doaj.org/article/e0f77d65a0db46d99d9cbd4349706212
Publikováno v:
International Journal of Digital Earth, Vol 16, Iss 1, Pp 620-644 (2023)
The point segmentation of power lines and towers aims to use unmanned aerial vehicles (UAVs) for the inspection of power facilities, risk detection and modelling. Because of the unclear spatial relationship between the point clouds, the point segment
Externí odkaz:
https://doaj.org/article/a18d18d255c541b8aa5b755f58531114
Publikováno v:
Sensors, Vol 24, Iss 17, p 5693 (2024)
This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual
Externí odkaz:
https://doaj.org/article/893563eb860044e1bd9328b454d296d4