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 …

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 …

Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

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 …

Text-driven 3D scene generation techniques have made rapid progress in recent years.
Their success is mainly attributed to using existing generative models to iteratively perform …

Perceiving 3D structures from RGB images based on CAD model primitives can enable an
effective, efficient 3D object-based representation of scenes. However, current approaches …

While methods for monocular depth estimation have made significant strides on standard
benchmarks, zero-shot metric depth estimation remains unsolved. Challenges include the …

Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks
in 3D perception and modeling. However the remarkable accuracy of recent MMDE methods …

Transferring the absolute depth prediction capabilities of an estimator to a new domain is a
task with significant real-world applications. This task is specifically challenging when …

Matching cross-modality features between images and point clouds is a fundamental
problem for image-to-point cloud registration. However, due to the modality difference …