3D Reconstruction of Plant/Tree Canopy Using Monocular and Binocular Vision

Abstract

Three-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area. In this research, binocular stereo vision was used to recover the 3D information of the canopy. Multiple images were taken from different views around the target. The Structure-from-motion (SfM) method was employed to recover the camera calibration matrix for each image, and the corresponding 3D coordinates of the feature points were calculated and used to recover the camera calibration matrix. Through this method, a sparse projective reconstruction of the target was realized. Subsequently, a ball pivoting algorithm was used to do surface modeling to realize dense reconstruction. Finally, this dense reconstruction was transformed to metric reconstruction through ground truth points which were obtained from camera calibration of binocular stereo cameras. Four experiments were completed, one for a known geometric box, and the other three were: a croton plant with big leaves and salient features, a jalapeno pepper plant with median leaves, and a lemon tree with small leaves. A whole-view reconstruction of each target was realized. The comparison of the reconstructed box’s size with the real box’s size shows that the 3D reconstruction is in metric reconstruction.

Plant phenotyping relevance

植物キャノピーの3D再構成手法を開発・検証し、高さ、幅、体積、葉面積などの形態形質の計測を目的としているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes 3D canopy reconstruction of a croton plant, jalapeno pepper plant, and lemon tree using VisualSFM/SiftGPU/Bundler/PMVS, but no author-deposited phenotype datasets, plant images, analysis code, or trained models are made available. All URLs in the text are third-party generic tools or cited priorwork

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