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

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.

Plant phenotyping relevance

果樹画像の分割と多視点ステレオによる3D形態再構成を中心に開発しており、植物体の構造・形態を取得するフェノタイピング手法に該当する。

Code and data availability

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