Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

Diffusion models (DMs) have become the new trend of generative models and have
demonstrated a powerful ability of conditional synthesis. Among those, text-to-image …

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

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 …

While state-of-the-art monocular depth estimation approaches achieve impressive results in
ideal settings, they are highly unreliable under challenging illumination and weather …

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 …

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 …

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and
a core technique for many downstream applications. Recently, several methods reframe the …

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