Accurate, Detailed and Automatic Tree Modelling from Point Clouds

Abstract

Trees are of great significance throughout the world, both in urban scenes and in natural environments. Models of trees can be widely applied in various fields, for instance, landscape design, geo-simulation, environment modelling, and forestry inventories. Recently, laser scanning technology has been rapidly developed, making it possible to effectively acquire geometric attributes of trees and achieve accurate 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. Nevertheless, some works don’t ensure sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user interactions. In this thesis, we propose a novel method to accurately and automatically reconstruct tree branches from laser scanned points. We first employ the Minimum Spanning Tree (MST) algorithm to extract an initial tree skeleton over the single tree point cloud, then simplify the skeleton through iterative removal of redundant components. A global-optimization approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. The results show that our approach is adaptable to various trees with different data qualities. We also demonstrate both the topological fidelity and geometrical accuracy of our approach without significant user interactions. The resulted tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc.