In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

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

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

Multi-frame depth estimation improves over single-frame approaches by also leveraging
geometric relationships between images via feature matching, in addition to learning …

Multi-view depth estimation has achieved impressive performance over various
benchmarks. However almost all current multi-view systems rely on given ideal camera …

In recent years, unmanned aircraft systems (UASs) have played an increasingly significant
role in the military and civil fields. The flight control system, as the “hub” of an unmanned …

Multi-view depth estimation methods typically require the computation of a multi-view cost-
volume, which leads to huge memory consumption and slow inference. Furthermore, multi …

In the last decade, numerous supervised deep learning approaches have been proposed for
visual–inertial odometry (VIO) and depth map estimation, which require large amounts of …

Depth estimation from a camera is an important task for 3D perception. Recently, without
using the labeled ground truth of depth map, a self-supervised deep learning network can …

Estimation of depth in two-dimensional images is among the challenging topics in Computer
Vision. This is a well-studied but also an ill-posed problem, which has long been the focus of …