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 …

Light field (LF) cameras record both intensity and directions of light rays, and encode 3D
scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have …

As one of the fundamental technologies for scene understanding, semantic segmentation
has been widely explored in the last few years. Light field cameras encode the geometric …

Global context information is particularly important for comprehensive scene understanding.
It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent …

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 …

Recently, numerous algorithms have been developed to tackle the problem of light field
super-resolution (LFSR), ie, super-resolving low-resolution light fields to gain high …

Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution
(SR), but is highly challenging due to its non-local property caused by the disparities among …

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

Matching cost construction is a key step in light field (LF) depth estimation, but was rarely
studied in the deep learning era. Recent deep learning-based LF depth estimation methods …

Light fields are vector functions that map the geometry of light rays to the corresponding
plenoptic attributes. They describe the holographic information of scenes by representing …