Elevation Angle Estimation in 2D Acoustic Images Using Pseudo Front View

Abstract

A novel method to estimate the missing dimension in 2D acoustic images for 3D reconstruction is proposed in this paper. Acoustic cameras can acquire high resolution 2D images in underwater environment insusceptible to water turbidity and light condition. However, the formulation of acoustic images leads to the missing dimension problem. Estimating the unknown elevation angle dimension is a difficult task which has recently drawn the attention of researchers. The non-bijective characteristic between 3D points and 2D pixels increases the complexity of the problem. In this paper, a novel elevation angle estimation method is proposed. The method transfers the acoustic view to pseudo front view using a deep neural network. The proposed network can estimate the missing dimension and resolve the non-bijection problem of the 2D-3D correspondence. Because of the difficulty of acquiring depth information in underwater environments, the network is trained using simulated images. To mitigate the sim-real gap, a neural style transfer method is implemented to generate a realistic image dataset for training. Simulation experiments were carried out for evaluation and real data proved the feasibility of the proposed method.