Tensor displays are multiview glasses-free 3D displays composed of a stack of LCD layers. The images displayed on each layer are usually generated from a dense set of viewpoints (light field) or a set of focused images (focal stack). This paper presents an alternative using a single color view with depth for the generation of the images’ layers. To do so various single-RGBD pipelines using intermediate data formats to generate the layers’ images, including a direct method, are compared. Experiments show that by a single-color view with depth as input, one can provide a quality close to the light field or focal stack pipelines, hence reducing memory storage costs.

3D layered displays are composed of a backlight and multiple LCD panels. To reproduce a 3D scene with such displays, a light field is given as input to optimize the layers' images that are displayed by each LCD panel. Current works optimize the layers using parallel rays (orthographic camera model) or do not take into account the distance of the user during the layers' optimization. In this paper, we present the use of the perspective model during the layer's optimization and guidelines for camera parameters based on the functioning of these displays. By using a more realistic camera model during the layers' optimization, a higher quality rendering is expected for close and middle viewing distances while giving similar quality for far distances. In the experiments, we observe that cameras' positioning can be adjusted to enhance the quality of high-parallax light fields. As a final result, the proposed method achieves higher quality on closer ranges to the 3D display when compared to the conventional method using parallel rays.

The plenoptic 2.0 camera is a light field acquisition system consisting of a main lens and a micro-lens array (MLA) at a non-focal distance of the main lens. While it allows to retrieve the geometry of the scene, the distances between the main lens, the MLA and the sensor are usually unknown. Therefore, the use cases for plenoptic cameras stay limited while they have more potential applications such as virtual reality, provided that their camera parameters are precisely known. In this paper, we present a pattern-free calibration method to retrieve the plenoptic camera's intrinsic parameters from the rendered subaperture images, i.e. the distance from main lens to the MLA and sensor, the focal length and the principal point. The proposed method utilises the relation between scene's distances, i.e. depth, and the subaperture images' micro-disparities, which contains information about the camera parameters. To the best of our knowledge, it is the first pattern-free calibration method for plenoptic 2.0 cameras. We compare the parameters obtained using our method applied to subaperture images rendered from three different software tools (Plenoptic Toolbox, Reference Lenslet content Convertor, and Lenslet to Multiview) with an open-source pattern-based method (Compote). The proposed pattern-free calibration method has consistent camera parameters with the traditional pattern-based calibration method. We therefore reliably obtain the intrinsic parameters of any plenoptic camera from its captured images, even in the absence of any calibration pattern.

A tensor display is a type of 3D light field display, composed of multiple transparent screens and a back-light that can render a scene with correct depth, allowing to view a 3D scene without wearing glasses. The analysis of state-of-the-art tensor displays assumes that the content is Lambertian. In order to extend its capabilities, we analyze the limitations of displaying non-Lambertian scenes and propose a new method to factorize the non-Lambertian scenes using disparity analysis. Moreover, we demonstrate a new prototype of a tensor display with three layers of full HD content at 60 fps. Compared with state-of-the-art, the evaluation results verify that the proposed non-Lambertian rendering method can display a higher quality for non-Lambertian scenes on both simulation and a prototyped tensor display.

We present a novel methodology to precisely calibrate the subaperture views of an array of plenoptic 2.0 cameras. Such cameras consist of a micro lens array, and the image captured through them is a lenslet image that can be converted to a dense set of pinhole views, the so-called subaperture images. This cam-era array provides several dense multiview images at some sparse points of 3D space. To find the relative position of those views, simply using structure-from-motion creates misalignments due to the small disparities within each set. Additionally, a traditional calibration using calibration patterns will also fail due to the complicated objectives of plenoptic 2.0 cameras and artifacts when they are converted to subaperture views. In this paper, we propose two calibration steps (a) to register the sparse central subaperture views using Structure-from-Motion which makes it robust to artifacts in the subaperture views, and (b) to register all dense multiview sets per plenoptic camera using camera’s lenses specifications, disparity and distance to the scene. These two steps are followed by a novel merging process of the former registrations, to achieve precise calibration parameters for all the subaperture views of the multi-plenoptic array. Experimental results objectively and subjectively demonstrate high accuracy of the calibration. We show a 10% smaller reprojection error than using a naive structure-from-motion approach and verify that our method is suitable for high precision view synthesis applications such as virtual reality and holography.