ElevateNet: A Convolutional Neural Network for Estimating the Missing Dimension in 2D Underwater Sonar Images

Abstract

In this work we address the challenge of predicting the missing dimension (elevation angle) from 2D underwater sonar images. The high noise levels in these images, from phenomena such as non-diffuse reflections, frequently limits the usefulness of physical models. We thus propose the utilization of Convolutional Neural Networks (CNNs) as a powerful method to extract meaningful information without being misled by noisy data. We also introduce a self-supervised method that uses the physics of the sonar sensor to train the network on real data without ground-truth elevation maps. Our method can produce accurate elevation angle estimates given only a single image. Finally, we demonstrate that our method produces more accurate 3D reconstructions than competing methods, both in simulation and on real data.