Monocular depth estimation is fundamental for 3D scene understanding and downstream
applications. However, even under the supervised setup, it is still challenging and ill-posed …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …

Monocular depth estimation has drawn widespread attention from the vision community due
to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep …

Abstract Three-dimensional (3D) reconstruction from a single image is an ill-posed problem
with inherent ambiguities ie scale. Predicting a 3D scene from text description (s) is similarly …

Self-supervised monocular depth estimation has shown impressive results in static scenes. It
relies on the multi-view consistency assumption for training networks, however, that is …

Self-supervised methods play an increasingly important role in monocular depth estimation
due to their great potential and low annotation cost. To close the gap with supervised …

Existing self-supervised monocular depth estimation methods can get rid of expensive
annotations and achieve promising results. However, these methods suffer from severe …

Self-supervised depth estimation for indoor environments is more challenging than its
outdoor counterpart in at least the following two aspects:(i) the depth range of indoor …

The majority of prior monocular depth estimation methods without groundtruth depth
guidance focus on driving scenarios. We show that such methods generalize poorly to …

Remarkable results have been achieved by DCNN based self-supervised depth estimation
approaches. However, most of these approaches can only handle either day-time or night …