Depth estimation and 3D object detection are critical for scene understanding but remain
challenging to perform with a single image due to the loss of 3D information during image …

Estimating depth from a single RGB images is a fundamental task in computer vision, which
is most directly solved using supervised deep learning. In the field of unsupervised learning …

Monocular depth estimation remains a challenging problem, despite significant advances in
neural network architectures that leverage pictorial depth cues alone. Inspired by depth from …

With the rapid development of vision sensing, artificial intelligence, and robotics technology,
one of the challenges we face is installing more advanced vision sensors on welding robots …

Homogeneous regions, which are smooth areas that lack blur clues to discriminate if they
are focused or non-focused. Therefore, they bring a great challenge to achieve high …

Abstract Depth-from-defocus (DFD), modeling the relationship between depth and defocus
pattern in images, has demonstrated promising performance in depth estimation. Recently …

Monocular depth estimation and image deblurring are two fundamental tasks in computer
vision, given their crucial role in understanding 3D scenes. Performing any of them by …

Autofocus is an important task for digital cameras, yet current approaches often exhibit poor
performance. We propose a learning-based approach to this problem, and provide a …

Estimating depth and defocus maps are two fundamental tasks in computer vision. Recently,
many methods explore these two tasks separately with the help of the powerful feature …

In this paper, we provide a survey on automotive surround-view fisheye optics, with an
emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving …