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 …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

Depth estimation from monocular images is pivotal for real-world visual perception systems.
While current learning-based depth estimation models train and test on meticulously curated …

Current, self-supervised depth estimation architectures rely on clear and sunny weather
scenes to train deep neural networks. However, in many locations, this assumption is too …

Multiple near frontal-parallel planes based depth representation demonstrated impressive
results in self-supervised monocular depth estimation (MDE). Whereas, such a …

In recent years the field of image dehazing has garnered increasing attention. Many deep
learning models have demonstrated exceptional capabilities in removing homogeneous …

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

Unsupervised depth completion aims to recover dense depth from the sparse one without
using the ground-truth annotation. Although depth measurement obtained from LiDAR is …

Recovering a clear image from a single hazy image is an open inverse problem. Although
significant research progress has been made most existing methods ignore the effect that …

Monocular depth estimation has received more and more attention due to its wide range of
application scenarios. In this paper, we propose a novel simple framework, called CATNet …