Light field imaging has emerged as a technology allowing to capture richer visual
information from our world. As opposed to traditional photography, which captures a 2D …

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) images acquired by hand-held devices usually suffer from low spatial
resolution as the limited sampling resources have to be shared with the angular dimension …

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 …

Capturing the directions of light by light field cameras powers next-generation immersive
multimedia applications. A critical problem in taking advantage of the rich visual information …

Depth estimation is a fundamental problem for light field photography applications.
Numerous methods have been proposed in recent years, which either focus on crafting cost …

Light Field (LF) cameras capture rich information in 4D LF images by recording both
intensity and angular directions, making it crucial to learn the inherent spatial-angular …

Depth estimation is a fundamental issue in 4-D light field processing and analysis. Although
recent supervised learning-based light field depth estimation methods have significantly …

Raining is a common weather condition, and may seriously degrade the performances of
outdoor computer vision systems, such as surveillance and autonomous navigation. Rain …

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 …