2D-projected tree model reconstruction from monocular images and DNN

Karim Ben Alaya; László Czúni

doi:10.1117/12.2623872

4 March 2022 2D-projected tree model reconstruction from monocular images and DNN

Karim Ben Alaya, László Czúni

Author Affiliations +

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 1208417 (2022) https://doi.org/10.1117/12.2623872
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

The delineation of plants and trees and their structural analysis is getting more important in agricultural and ecological applications. In our paper, we propose an approach where 2D-projected graph-based tree models are generated from monocular images with the help of deep neural networks (DNN): the three main blocks of the network are responsible for segmentation, contour, and centerline detection. Thus graph structures are built upon these predicted structural elements. We demonstrate that the applied DNN can also help to reconstruct the spatial (depth) order of crossing branches. The proposed method is believed to have the potential to soon replace current expensive and timeconsuming laser scanning approaches for many applications.

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Karim Ben Alaya and László Czúni "2D-projected tree model reconstruction from monocular images and DNN", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 1208417 (4 March 2022); https://doi.org/10.1117/12.2623872

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