This is a follow-up of the grand challenge with the same title, which was organized at IEEE ICME 2018. Our aim now is to refine it and to introduce more challenging datasets.

Densely-sampled light field (DSLF) is a discrete representation of the 4D approximation of the plenoptic function parameterized by two parallel planes (camera plane and image plane), where multi-perspective camera views are arranged in such a way that the disparities between adjacent  views are less than one pixel. DSLF allows generating any desired light ray along the parallax axis by simple local interpolation. DSLF capture setting in terms of physical camera locations depends on the minimal scene depth and the camera sensor resolution.  Direct DSLF capture of real-world scenes requires a very high number of densely located cameras, which is not practical. This motivates the problem of DSLF reconstruction from a given sparse set of camera images utilizing the properties of the scene objects and the underlying plenoptic function. 

The goal of this challenge is two-fold:

First, to generate high-quality and meaningful DSLF datasets for further experiments

Second, to quantify the state of the art in the area of light field reconstruction and processing in order to provide instructive results about the practical LF capture settings in terms of number of cameras and their relative locations. This will be furthermore helpful in applications, aiming at:

• Predicting intermediate views from neighboring views in LF compression
• Generating high-quality content for super-multiview and LF displays
• Providing FVV functionality
• Converting LF (I.e. ray optics based) representation into holographic (I.e. wave optics based) representations for the needs of digital hologram generation

Proponents are asked to develop and implement algorithms for DSLF reconstruction from decimated-parallax imagery in three categories:

• Cat1: close camera views along parallax axis resulting in adjacent images with narrow disparity (e.g. in the range of 8 pixels)
• Cat2: moderately-distant cameras along parallax axis, resulting in adjacent images with moderate disparities (in the range of 15-16 pixels)
• Cat3: distant cameras, resulting in adjacent images with wide disparity (in the range of 30-32 pixels)

Algorithms in each category will be evaluated separately, thus resulting in three sub-challenges. Proponents are invited to submit solutions to one or more categories.