Terrestrial Image Based 3d Extraction of Urban Unfoliaged Trees of Different Branching Types

In this paper we propose extensions to a generative statistical approach for three-dimensional (3D) extraction of urban unfoliaged trees of different branching types from terrestrial wide-baseline image sequences. Unfoliaged trees are difficult to extract from images due to their weak contrast, background clutter, and particularly the possibly varying order of branches in different images. By combining generative modeling by L-systems and statistical sampling one can reconstruct the main branching structure of trees in 3D based on image sequences in spite of these problems. Here, we particularly classify trees into different branching types and specific L-systems are applied for each type for a more plausible description. We combine Monte Carlo (MC) with subsequential Markov Chain Monte Carlo (MCMC) to robustly and efficiently deal with the sparse distributions of the branching parameters. First results show the potential of the extended approach.