  Retrieving the missing dimension information in acoustic images from 2D
forward-looking sonar is a well-known problem in the field of underwater
robotics. There are works attempting to retrieve 3D information from a single
image which allows the robot to generate 3D maps with fly-through motion.
However, owing to the unique image formulation principle, estimating 3D
information from a single image faces severe ambiguity problems. Classical
methods of multi-view stereo can avoid the ambiguity problems, but may require
a large number of viewpoints to generate an accurate model. In this work, we
propose a novel learning-based multi-view stereo method to estimate 3D
information. To better utilize the information from multiple frames, an
elevation plane sweeping method is proposed to generate the
depth-azimuth-elevation cost volume. The volume after regularization can be
considered as a probabilistic volumetric representation of the target. Instead
of performing regression on the elevation angles, we use pseudo front depth
from the cost volume to represent the 3D information which can avoid the 2D-3D
problem in acoustic imaging. High-accuracy results can be generated with only
two or three images. Synthetic datasets were generated to simulate various
underwater targets. We also built the first real dataset with accurate ground
truth in a large scale water tank. Experimental results demonstrate the
superiority of our method, compared to other state-of-the-art methods.

Computer Vision and Pattern Recognition;Robotics