Stefan M., Sebastian Süss, Tobias Rüdiger, Florian Ölsner, Friedrich Möller, Mateusz Olichwer, B Ravi Kiran, Isabelle Leang, Senthil Yogamani
Abstract: Recently, aerial LiDAR data opened lots of new opportunities for many research disciplines like macroscopic geophysical analysis or archaeological investigations. However, LiDAR measurements are expensive and the data is not widely distributed or accessible. We propose a novel method for image to image translation performing HD-LiDAR reconstruction using RGB input images based on conditional GANs. The conditional mapping function of the generator G : [c, z] → y is transformed to G : [x, z] → y, whereas y represents the reconstructed LiDAR map and c represents the condition. c is replaced by the aligned aerial camera image x. z represents the noise. Our approach is able to reconstruct LiDAR data as elevation maps based on small scaled training data, which includes RGB and LiDAR sample pairs based on 256 × 256 image matrices. The model offers the opportunity to complete geophysical LiDAR databases, where measurements are missing. The method is validated on the ISPRS dataset with an overall rRMSE of 14.53%.
