Abstract
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.
Highlights
Three-dimensional (3-D) displays have been the subject of study for many years [1]–[5]
Regarding the optimization of the layer patterns, we compared analytical methods used in previous studies [16], [21], which are slow due to heavy computation, with faster data-driven methods implemented as convolutional neural networks (CNNs) [25]
Our results indicate that multiplicative layers achieve better accuracy than additive ones, and CNN-based methods are faster than the analytical methods, with comparable accuracy to the best achievable accuracy of analytical methods
Summary
Three-dimensional (3-D) displays have been the subject of study for many years [1]–[5]. A light field [18], [19] (i.e., tens of images), which is expected to be observed from different viewing directions, is given as the input, the layer patterns are optimized to reproduce the light field as accurately as possible This design can be applied to light-field projections [20], [21], headmounted displays [22], [23], and table-top displays [24]. Regarding the optimization of the layer patterns, we compared analytical methods used in previous studies [16], [21], which are slow due to heavy computation, with faster data-driven methods implemented as convolutional neural networks (CNNs) [25] Such a CNN-based method requires significant time for training, but inference using a trained network is very fast, which paves the way for light-field displays running at video-rate speed. We describe the coordinate system we used in this study
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