A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images

The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D b...

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Published in:Mathematical biosciences and engineering : MBE Vol. 20; no. 12; pp. 21432 - 21450
Main Author: Li, Xiaoli
Format: Journal Article
Language:English
Published: United States AIMS Press 01.01.2023
Subjects:
3d reconstruction
image processing
kd-tree algorithm
random sample consensus
image processing
3D reconstruction
KD-tree algorithm
random sample consensus
ISSN:1551-0018, 1551-0018
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Abstract The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.
AbstractList The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.
The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.The application of 3D reconstruction technology in building images has been a novel research direction. In such scenes, the reconstruction with proper building details remains challenging. To deal with this issue, I propose a KD-tree and random sample consensus-based 3D reconstruction model for 2D building images. Specifically, the improved KD-tree algorithm with the random sampling consistency algorithm has a better matching rate for the two-dimensional image data extraction of the stadium scene. The number of discrete areas in the stadium scene increases with the increase in the number of images. The sparse 3D models can be transformed into dense 3D models to some extent using the screening method. In addition, we carry out some simulation experiments to assess the performance of the proposed algorithm in this paper in terms of stadium scenes. The results reflect that the error of the proposal is significantly lower than that of the comparison algorithms. Therefore, it is proven that the proposal can be well-suitable for 3D reconstruction in building images.
Author Li, Xiaoli
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SubjectTerms 3d reconstruction
image processing
kd-tree algorithm
random sample consensus
Title A KD-tree and random sample consensus-based 3D reconstruction model for 2D sports stadium images
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