LiDAR data reduction assisted by optical image for 3D building reconstruction

Yang, Lin; Sheng, Yehua; Wang, Bo

doi:10.1016/j.ijleo.2014.08.016

Cited by 6 publications

(3 citation statements)

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“…S image x j , y j = α (13) where the parameter α is against the background; for example, the parameter α is 0 and the background is 1 in the image. Therefore, we obtain two sets of data: the two-dimensional points with a grid structure and the corresponding images of the two-dimensional points.…”

Section: Results Detailsmentioning

confidence: 99%

“…In recent years, LiDAR (light detection and ranging) technology has developed rapidly due to its high accuracy, low cost, portability, and wide application range such as in autonomous driving [1][2][3][4][5], military fields [6][7][8][9], aerospace [10,11], and three-dimensional (3D) reconstruction [12][13][14]. In terms of 3D modeling, high-precision and high-density point cloud data are provided by LiDAR to accurately restore the surface model of an object that could be a trunk [15], a geological landform [16], or a building [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Extraction of Indoor Structural Information from Point Clouds

et al. 2021

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We propose an innovative method with which to extract building interior structure information automatically, including ceiling, floor, and wall. Our approach outperforms previous methods in the following respects. First, we propose an approach based on principal component analysis (PCA) to find the ground plane, which is regarded as the new Cartesian plane. Second, to reduce the complexity of data processing, the data are projected into two dimensions and transformed into a binary image via the operation of an improved radius outlier removal (ROR) filter. Third, a traditional thinning algorithm is adopted to extract the image skeleton. Then, we propose a method for calculating slope through the nearest neighbor point. Moreover, the line is represented with the slopes to obtain information pertaining to the interior planes. Finally, the outline of the line is restored to a three-dimensional structure. The proposed method is evaluated in multiple scenarios, and the results show that the method is accurate (the maximum error of 0.03 m was in three scenarios) in indoor environments.

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Section: Results Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Indoor Structural Information from Point Clouds

et al. 2021

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“…They proposed a reduction algorithm that based on the combination of mutual information (MI) with steepest ascent algorithm, to improve the symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HIS. Yang et al (2014) used an optical image for 3D building reconstruction to perform reduction for LIDAR data. The method consists of a sequence of three procedures: 2D feature line extraction, 3D features line converting and buffer area of LIDAR points.…”

Section: Introductionmentioning

confidence: 99%

Point clouds reduction model based on 3D feature extraction

Sayed

Taie

Khoribi

et al. 2019

IJES

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Light detection and ranging (LIDAR) is a remote sensing method that scans the Earth's surface with high density to construct the digital elevation model (DEM). In this paper, we present a point clouds reduction model based on two 3D feature extraction techniques, namely: the sharp feature detection algorithm and feature extraction technique-based LIDAR point attributes. These techniques are used as initial selection criteria and are compared with the maximum and the minimum elevation criterion that gives reduction with the highest accuracy. However, point clouds reduction algorithms lead to high consumption of time to generate a reduced file with high accuracy, which prompts the need to propose a new model that considers the trade-off between the processing time and the accuracy. The results showed that the proposed model significantly reduced the processing time at the expense of accuracy reduction by 0.7% and 1.3% for the two used techniques respectively, which is acceptable for realistic applications.

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