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

We propose MAMo, a novel memory and attention framework for monocular video depth
estimation. MAMo can augment and improve any single-image depth estimation networks …

Video depth estimation aims to infer temporally consistent depth. One approach is to
finetune a single-image model on each video with geometry constraints, which proves …

Depth estimation is a crucial task for the creation of depth maps, one of the most important
components for augmented reality (AR) and other applications. However, the most widely …

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 …

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor
data. The respectively used principle of measuring distances provides advantages and …

In this paper, we propose a novel video depth estimation approach, FutureDepth, which
enables the model to implicitly leverage multi-frame and motion cues to improve depth …

Depth estimation using monocular camera sensors is an important technique in computer
vision. Supervised monocular depth estimation requires a lot of data acquired from depth …

Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision,
plays important roles in downstream applications such as virtual reality, 3D reconstruction …

Self-supervised monocular depth estimation has gathered notable interest since it can
liberate training from dependency on depth annotations. In monocular video training case …