Metrically Scaled Monocular Depth Estimation through Sparse Priors for
  Underwater Robots
Luca Ebner Gideon Billings Stefan Williams
In this work, we address the problem of real-time dense depth estimation from
monocular images for mobile underwater vehicles. We formulate a deep learning
model that fuses sparse depth measurements from triangulated features to
improve the depth predictions and solve the problem of scale ambiguity. To
allow prior inputs of arbitrary sparsity, we apply a dense parameterization
method. Our model extends recent state-of-the-art approaches to monocular image
based depth estimation, using an efficient encoder-decoder backbone and modern
lightweight transformer optimization stage to encode global context. The
network is trained in a supervised fashion on the forward-looking underwater
dataset, FLSea. Evaluation results on this dataset demonstrate significant
improvement in depth prediction accuracy by the fusion of the sparse feature
priors. In addition, without any retraining, our method achieves similar depth
prediction accuracy on a downward looking dataset we collected with a diver
operated camera rig, conducting a survey of a coral reef. The method achieves
real-time performance, running at 160 FPS on a laptop GPU and 7 FPS on a single
CPU core and is suitable for direct deployment on embedded systems. The
implementation of this work is made publicly available at
https://github.com/ebnerluca/uw_depth.