View planning for 3D shape reconstruction of buildings with unmanned aerial vehicles

This paper presents a novel view planning method to generate suitable viewpoints for the reconstruction of the 3D shape of buildings, based on publicly available 2D map data. The proposed method first makes use of 2D map data, along with estimated height information, to generate a rough 3D model of the target building. Randomized sampling procedures are then employed to generate a set of initial candidate viewpoints for the reconstruction process. The most suitable viewpoints are selected from the candidate viewpoint set by first formulating a modified Set Covering Problem (SCP) which considers image registration constraints, as well as uncertainties present in the rough 3D model. A neighborhood greedy search algorithm is proposed to solve this SCP problem and select a series of individual viewpoints deemed most suitable for the 3D reconstruction task. The paper concludes with both computational and real-world field tests to demonstrate the overall effectiveness of the proposed method.