A benchmark of light field view interpolation methods

Abstract

Light field view interpolation provides a solution that reduces the prohibitive size of a dense light field. This paper examines state-ofthe-art light field view interpolation methods with a comprehensive benchmark on challenging scenarios specific for interpolation tasks. Each method is analyzed in terms of their strengths and weaknesses in handling different challenges. We find that large disparities in a scene are the main source of challenge for the light field view interpolation methods. We also find that a basic backward warping based on the depth estimation from optical flow provides comparable performance against usually complex learning-based methods.