We review solutions to the problem of depth estimation, arguably the most important subtask
in scene understanding. We focus on the single image depth estimation problem. Due to its …

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 …

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 from a single RGB image has attracted great interest in autonomous
driving and robotics. State-of-the-art methods are usually designed on top of complex and …

Temporally consistent depth estimation is crucial for online applications such as augmented
reality. While stereo depth estimation has received substantial attention as a promising way …

Photometric differences are widely used as supervision signals to train neural networks for
estimating depth and camera pose from unlabeled monocular videos. However, this …

During the mineral flotation process, the surface froth image contains characteristic
information that is closely related to the production index of the process. However, a delay …

Video depth estimation is crucial in various applications, such as scene reconstruction and
augmented reality. In contrast to the naive method of estimating depths from images, a more …

We present a method for estimating temporally stable depth video from a sequence of
images. We extend the prior work aimed at video depth estimation, Neural-RGBD [1], which …

Focus based methods have shown promising results for the task of depth estimation.
However, most existing focus based depth estimation approaches depend on maximal …