Functional Surface Reconstruction from Unorganized Noisy Point Clouds

Liu, Xueshu

doi:10.1080/16864360.2014.981467

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Bazazian et al [11] uses the region growth combined with geodesic distance for the region segmentation of point clouds, and defines a multi-scale operator to determine which feature points are continuous. By the same token, the normal vector difference of adjacent points can be used as a multi-scale operator to detect the feature points [12]. Xu et al [13] used the clustering characteristics of a neighborhood normal vector to segment the surface, and then merge the surface based on the surface normal vector and roughness to achieve the accurate segmentation of the surface.…”

Section: Related Workmentioning

confidence: 99%

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Chen,

An,

Zhao

et al. 2024

Drones

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The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.

show abstract

Section: Related Workmentioning

confidence: 99%

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Chen,

An,

Zhao

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

Surface detection and modeling of an arbitrary point cloud from 3D sketching

Schwartz

Schneor

Molcho

et al. 2017

Computer-Aided Design and Applications

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Daylighting not only provides alternative illumination to artificial lighting that can reduce energy consumption, it also can increase an inhabitant's productivity and help to relieve depression. Maximizing the access of daylight for an interior space in a dense urban environment has thus become a priority in lighting design. Digital lighting simulation using high dynamic range imaging for image-based lighting can provide accurate daylight simulation of an interior luminance distribution. However, the method of generating a high-dynamic range light probe image is limited to the setting of a flat landscape with an unobstructed view. This paper presents an innovative system that is implemented using a lightweight smartphone camera equipped with a fish-eye lens on a customized unmanned aerial vehicle (UAV). The system can be remotely controlled to loiter, recording multiple images in a horizontal direction. The captured images can then be assembled into a high dynamic range light probe image to be used as the image-based lighting source for interior spaces with onedirectional access to daylight. The pilot test proved that the light probe images generated with the constructed prototype and system provide reasonable accuracy in daylight simulation.

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Functional Surface Reconstruction from Unorganized Noisy Point Clouds

Cited by 2 publications

References 13 publications

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

Surface detection and modeling of an arbitrary point cloud from 3D sketching

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