Comparison of Layer Operations and Optimization Methods for Light Field Display

A light-field display provides not only binocular depth sensation but also natural motion parallax with respect to head motion, which invokes a strong feeling of immersion. Such a display can be implemented with a set of stacked layers, each of which has pixels that can carry out light-ray operations (multiplication and addition). With this structure, the appearance of the display varies over the observed directions (i.e., a light field is produced) because the light rays pass through different combinations of pixels depending on both the originating points and outgoing directions. To display a specific 3-D scene, these layer patterns should be optimized to produce a light field that is as close as possible to that produced by the target three-dimensional scene. To deepen the understanding for this type of light field display, we focused on two important factors: light-ray operations carried out using layers and optimization methods for the layer patterns. Specifically, we compared multiplicative and additive layers, which are optimized using analytical methods derived from mathematical optimization or faster data-driven methods implemented as convolutional neural networks (CNNs). We compared combinations within these two factors in terms of the accuracy of light-field reproduction and computation time. Our results indicate that multiplicative layers achieve better accuracy than additive ones, and CNN-based methods perform faster than the analytical ones. We suggest that the best choice in terms of the balance between accuracy and computation speed is using multiplicative layers optimized using a CNN-based method.