Point2Wave: 3D Point Cloud to Waveform Translation Using a Conditional Generative Adversarial Network with Dual Discriminators

Since 2017, many deep learning methods for 3D point clouds observed by airborne LiDAR (airborne 3D point clouds) have been proposed. Moreover, not only a deep learning method for airborne 3D point clouds but also a deep learning method for points and their waveforms observed by full-waveform LiDAR (airborne FW data) was proposed. We need to achieve highly accurate land cover classification by using airborne FW data, but open data often only have airborne 3D point clouds available. Therefore, to improve the performance of land cover classification when using airborne 3D point clouds published as open data, it is important to restore waveforms from airborne 3D point clouds. In this paper, we propose a deep learning model to translate an airborne 3D point cloud to airborne FW data (called a point-to-waveform translation model, point2wave) using a conditional generative adversarial net (cGAN). Our point2wave is a cGAN pipeline consisting of a generator that translates the waveform corresponding to each point from the input airborne 3D Point Cloud and discriminators that calculate the distance between the translated waveform and the ground truth waveform. Using a set of point clouds and waveforms dataset, we have experimented to translate points into the waveforms by point2wave. Experimental results showed that point2wave could translate waveforms from the airborne 3D point cloud and the translated fake waveforms achieved nearly the same land cover classification performance as the real waveforms.