Hybrid tree reconstruction from inhomogeneous point clouds

Trees are an important asset for natural-looking digital environments. We propose a novel method to automatically reconstruct tree geometry from inhomogeneous point clouds created by a laser scanner. While previous approaches focus either on dense or sparse point clouds, our hybrid method allows for the reconstruction of a tree from an inhomogeneous point cloud without further preprocessing. Using principal curvatures as indicators for branches, we detect ellipses in branch cross-sections and create branch skeletons for dense regions. For sparse regions we approximate branch skeletons with a spanning tree. Branch widths are obtained from the ellipse fitting in dense regions and propagated to the sparse regions, to create geometry for the whole tree. We demonstrate the effectiveness of our approach in several real-world examples.