To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

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 …

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 …

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 …

Light field can record the four-dimensional information of light rays, ie the position and
direction information in which depth information is implied. To improve the depth estimation …

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 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) cameras record both intensity and directions of light rays, and capture scenes
from a number of viewpoints. Both information within each perspective (ie, spatial …

Light field cameras are considered to have many potential applications since angular and
spatial information is captured simultaneously. However, the limited spatial resolution has …