Research on virtual fruit tree reconstruction method based on multiview stereo 3D reconstruction

Aiming at the problems of low fidelity, long time-consuming and high cost of constructing fruit tree models in the virtual orchard scene, this paper proposes a 3D reconstruction method of virtual fruit trees based on Structure from motion-Multi-view stereo (SFM-MVS). First, the fruit tree image acquisition is carried out by using camera, the SFM algorithm is used to calculate the camera parameters of the fruit tree pictures and the positional relationship between the cameras, the image segmentation is carried out by combining the Convolutional Neural Networks (CNN) of the deep learning, and the segmentation of the fruit tree and the background of the environment in the image is completed by using the DeepLab algorithm. Secondly, the MVS algorithm is used to fuse the segmented fruit tree information and the associated camera position information to automatically construct a high-precision 3D model of the fruit tree. Finally, the mesh information and texture mapping of the 3D model are imported into the Unity3D virtual simulation platform, and the attribute fusion is realized by Albedo, which realizes the rapid digital model construction of real fruit trees.

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Publication Details

Published
2025-02-05
DOI
https://doi.org/10.1117/12.3057785
Primary Topic
Remote Sensing and Land Use
Type
article
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article

Research on virtual fruit tree reconstruction method based on multiview stereo 3D reconstruction

Ying Wei, Mingxuan Zhou, Hongjun Wang, Bi Luchao
Remote Sensing and Land Use
article

Research on virtual fruit tree reconstruction method based on multiview stereo 3D reconstruction

Ying Wei, Mingxuan Zhou, Hongjun Wang, Bi Luchao
article en

Abstract

Aiming at the problems of low fidelity, long time-consuming and high cost of constructing fruit tree models in the virtual orchard scene, this paper proposes a 3D reconstruction method of virtual fruit trees based on Structure from motion-Multi-view stereo (SFM-MVS). First, the fruit tree image acquisition is carried out by using camera, the SFM algorithm is used to calculate the camera parameters of the fruit tree pictures and the positional relationship between the cameras, the image segmentation is carried out by combining the Convolutional Neural Networks (CNN) of the deep learning, and the segmentation of the fruit tree and the background of the environment in the image is completed by using the DeepLab algorithm. Secondly, the MVS algorithm is used to fuse the segmented fruit tree information and the associated camera position information to automatically construct a high-precision 3D model of the fruit tree. Finally, the mesh information and texture mapping of the 3D model are imported into the Unity3D virtual simulation platform, and the attribute fusion is realized by Albedo, which realizes the rapid digital model construction of real fruit trees.

South China Agricultural University (CN)
Sustainable cities and communities
Openalex Percentile: Top 98%
Remote Sensing and Land Use
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