Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

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 …

We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses
on core visual perception abilities not found in other evaluations. Most of the Blink tasks can …

Abstract Neural Radiance Field (NeRF) significantly degrades when only a limited number
of views are available. To complement the lack of 3D information, depth-based models, such …

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 …

Virtual reality and augmented reality (XR) bring increasing demand for 3D content
generation. However, creating high-quality 3D content requires tedious work from a human …

Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-
posedness of the single-image reconstruction problem, most well-established methods are …

Computer vision now relies on data, but we know surprisingly little about what factors in the
data affect performance. We argue that this stems from the way data is collected. Designing …

Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …