Abstract Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a single
RGB image. For both, the convolutional as well as the recent attention-based models …

Depth estimation from a single image is an important task that can be applied to various
fields in computer vision, and has grown rapidly with the development of convolutional …

We introduce AiT, a unified output representation for various vision tasks, which is a crucial
step towards general-purpose vision task solvers. Despite the challenges posed by the high …

Current, self-supervised depth estimation architectures rely on clear and sunny weather
scenes to train deep neural networks. However, in many locations, this assumption is too …

This work aims to estimate a high-quality depth map from a single RGB image. Due to the
lack of depth clues, making full use of the long-range correlation and local information is …

We compare five monocular depth estimation methods based on deep learning. This
comparison focuses on how well methods generalize rather than a quantitative comparison …

This paper discusses the results for the second edition of the Monocular Depth Estimation
Challenge (MDEC). This edition was open to methods using any form of supervision …

In the absence of parallax cues a learning-based single image depth estimation (SIDE)
model relies heavily on shading and contextual cues in the image. While this simplicity is …

Depth estimation is a fundamental knowledge for autonomous systems that need to assess
their own state and perceive the surrounding environment. Deep learning algorithms for …

Data augmentation (DA) is an effective way to improve the performance of deep networks.
Unfortunately, current methods are mostly developed for high-level vision tasks (eg, image …