Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots
Luca Ebner,Gideon Billings,Stefan Williams,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 ex...
