Generative statistical 3D reconstruction of unfoliaged trees from terrestrial images

Abstract

This article presents a generative statistical approach for the automatic three-dimensional (3D) extraction and reconstruction of unfoliaged deciduous trees from terrestrial wide-baseline image sequences. Unfoliaged trees are difficult to reconstruct from images because of partially weak contrast, background clutter, occlusions, and particularly the possibly varying order of branches in images from different viewpoints. This work combines generative modeling by L-systems and a statistical approach for maximum a posteriori estimation for the reconstruction of the 3D branching structure of trees. Background estimation is conducted by means of gray scale morphology to provide a good basis for generative modeling. A Gaussian likelihood function based on intensity differences is used to evaluate the hypotheses. The target tree is classified into three typical branching types after the extraction of the first level of branches and specific production rules of an L-system are used. Generic prior distributions for parameters are refined based on already extracted branches in a Bayesian framework and are integrated into the maximum a posteriori estimation. By these means most of the branching structure besides the tiny twigs can be reconstructed. The results are presented in the form of virtual reality modeling language models and show the potential of the approach.