This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at
predicting disparity information of central view image in a light field (ie, pixel offset between …

In this report we summarize the 2nd NTIRE challenge on light field (LF) image super-
resolution (SR) with a focus on new methods and results. This challenge aims at super …

In this report, we summarize the first NTIRE challenge on light field (LF) image super-
resolution (SR), which aims at super-resolving LF images under the standard bicubic …

Compared with external-supervised learning-based (ESLB) methods, self-supervised
learning-based (SSLB) methods can overcome the domain gap problem caused by different …

Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing
methods are based on the Lambertian assumption and cannot deal with non-Lambertian …

Light Field (LF) depth estimation is an important research direction in the area of computer
vision and computational photography, which aims to infer the depth information of different …

Since light field simultaneously records spatial information and angular information of light
rays, it is considered to be beneficial for many potential applications, and semantic …

Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing
LF depth estimation methods consider depth estimation as a regression problem, where a …

The disparity information reflects pixel-wise inter-view correlations among sub-aperture
images (SAIs) of a light field (LF) image. Existing CNN-based methods for LF spatial super …

Most existing light field (LF) disparity estimation algorithms focus on handling occlusion,
texture-less or other areas that harm LF structure to improve accuracy, while ignoring other …