论文标题
光场重建的空间角度注意网络
Spatial-Angular Attention Network for Light Field Reconstruction
论文作者
论文摘要
通过加深网络以捕获输入视图之间的对应关系，基于典型的基于学习的光场重建方法在构建大型接收场时需要。在本文中，我们提出了一个空间 - 角度注意网络，以非局部的光场感知对应关系，并以端到端方式重建高角度分辨率光场。引起了专门针对高维光场数据的空间 - 角度注意模块的动机，以计算光场中每个像素的异性平面中所有位置的响应，并产生一个沿角尺寸捕获对应关系的注意力图。然后，我们提出了一个多尺度的重建结构，以在低空间尺度上有效地实现非本地关注，同时还保留高空间尺度中的高频组件。广泛的实验表明，提出的空间角度注意网络的出色表现，用于重建具有非lambertian效应的稀疏采样光场。
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.
