Diving Into Epipolar Transformers for Light Field Super-Resolution and Disparity Estimation

Light field (LF) cameras capture the light rays of a 3D scene from multiple views simultaneously, and thus provide a more immersive experience of the real world as compared to traditional cameras. Although significant progress has been made in various LF image processing tasks, it remains challenging to effectively model the non-local spatial-angular correlations inherent in LF images, particularly when dealing with complex disparity variations. In this paper, we focus on orthogonal epipolar geometry of LF images and propose a generic Epipolar Transformer mechanism that incorporates geometrically meaningful correlations along the epipolar lines. Our Epipolar Transformer mechanism enjoys the following benefits: learning effective and diverse LF feature representations, delivering satisfactory results without redundant architectural designs, and enabling flexible extension to various LF-related tasks with simple adaptations. For LF spatial and angular super-resolution, our methods not only achieve state-of-the-art performance on benchmark datasets, but also demonstrate superior and robust performance on large disparity variations. For disparity estimation, we explore the use of geometry information encoded in our Epipolar Transformer to directly regress the disparity results, effectively avoiding the limitation of a fixed maximum disparity.