Oblique photogrammetry supporting procedural tree modeling in urban areas

Abstract

Trees are common natural objects widely distributed in urban areas. The modeling quality of trees has significant visual effects for urban modeling. Tree models directly from the oblique photogrammetry pipeline are erratic because tree textures are repeating and confusing. Existing tree modeling approaches either cannot deal with the input data of poor quality or are time-consuming. This paper presents an oblique photogrammetry-supporting procedural tree modeling approach to solve this problem. Specifically, we propose a point cloud data-supported hybrid parametric model that models trees by simulating the growth of natural trees. To solve the challenging optimization problem in high dimensional space defined by the parametric model, a Control Parameter Analysis (CPA)-based optimization method is proposed to find the approximate solution with the highest resemblance to the input data. Finally, an automatic level of detail (LOD) control method of tree models is proposed to form a complete workflow for rendering the urban scenes. Experimental results indicate that the proposed method can generate procedural tree models in urban areas with satisfying accuracy and efficiency. The mean normalized distance between the generated tree models and input data in the test regions is 0.022, and it takes about ten minutes to generate each tree model. Our code is available at https://github.com/lelleMU/Procedrual_Tree_Modeling.