Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth
from a single image is geometrically ill-posed and requires scene understanding so it is not …

Monocular depth estimation (MDE) is a fundamental task in computer vision and has drawn
increasing attention. Recently, some methods reformulate it as a classification-regression …

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to
produce metric predictions. Even so, the resulting models will be geometry-specific, with …

Majority of research in learning based methods has been towards designing and training
networks for specific tasks. However, many of the learning based tasks, across modalities …

Neural-network-based single image depth prediction (SIDP) is a challenging task where the
goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is …

Multi-modal fusion is a promising approach to boost the autonomous driving performance
and has already received a large amount of attention. Meanwhile, to increase driving …

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 …

The emergence of large-scale high-quality datasets has stimulated the rapid development of
deep learning in recent years. However, most computer vision tasks focus on the visual …

Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed
problem in computer vision. While classical depth estimation methods infer only a 2.5 D …

Monocular depth estimation has experienced significant progress on terrestrial images in
recent years thanks to deep learning advancements. But it remains inadequate for …