Spatial-Angular Attention Network for Light Field Reconstruction

TL;DR

This paper introduces a spatial-angular attention network that captures non-local correspondences in light fields, enabling high-quality reconstruction of high angular resolution light fields efficiently and effectively.

Contribution

It presents a novel spatial-angular attention module and a multi-scale reconstruction structure for improved light field reconstruction.

Findings

Outperforms existing methods in reconstructing sparse light fields

Effectively captures non-Lambertian effects

Preserves high-frequency details during reconstruction

Abstract

Typical learning-based light field reconstruction methods demand in constructing a large receptive field by deepening the network to capture correspondences between input views. In this paper, we propose a spatial-angular attention network to perceive correspondences in the light field non-locally, and reconstruction high angular resolution light field in an end-to-end manner. Motivated by the non-local attention mechanism, a spatial-angular attention module specifically for the high-dimensional light field data is introduced to compute the responses from all the positions in the epipolar plane for each pixel in the light field, and generate an attention map that captures correspondences along the angular dimension. We then propose a multi-scale reconstruction structure to efficiently implement the non-local attention in the low spatial scale, while also preserving the high frequency components in the high spatial scales. Extensive experiments demonstrate the superior performance of the proposed spatial-angular attention network for reconstructing sparsely-sampled light fields with non-Lambertian effects.