We present a novel, first-of-its-kind view synthesis method for plenoptic images, which enables the direct manipulation of images in the micro-images array format, thereby bypassing intermediate transformation steps. Current plenoptic imaging approaches typically rely on an initial conversion to dense multiview images, also known as subaperture images extraction. However, the use of subaperture images presents two main limitations that ultimately impact further processing. First, existing subaperture view extraction methods offer limited control over camera parameters, resolutions, and poses of the subaperture views, which are also constrained to a small area around the main lens, thus restricting free navigation. Second, subaperture images are susceptible to artifacts which can propagate to subsequent processes such as calibration, depth estimation and view synthesis. In this paper, we propose a camera model that enables depth image-based rendering with plenoptic cameras, in a way that allows for the direct synthesis of any target viewpoint. In our evaluation, we show that our method expands view synthesis extrapolation to a range that is two to three times greater than that of pipelines requiring a conversion to subaperture images, including generally accepted tools such as depth image-based rendering and learning-based rendering approaches.

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model’s ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

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.

Light field displays project hundreds of micro-parallax views for users to perceive 3D without wearing glasses. It results in gigantic bandwidth requirements if all views would be transmitted, even using conventional video compression per view. MPEG Immersive Video (MIV) follows a smarter strategy by transmitting only key images and some metadata to synthesize all the missing views. We developed (and will demonstrate) a real-time Depth Image Based Rendering software that follows this approach for synthesizing all light field micro-parallax views from a couple of RGBD input views.

Non-Lambertian objects present an aspect which depends on the viewer's position towards the surrounding scene. Contrary to diffuse objects, their features move non-linearly with the camera, preventing rendering them with existing Depth Image-Based Rendering (DIBR) approaches, or to triangulate their surface with Structure-from-Motion (SfM). In this paper, we propose an extension of the DIBR paradigm to describe these non-linearities, by replacing the depth maps by more complete multi-channel “non-Lambertian maps”, without attempting a 3D reconstruction of the scene. We provide a study of the importance of each coefficient of the proposed map, measuring the trade-off between visual quality and data volume to optimally render non-Lambertian objects. We compare our method to other state-of-the-art image-based rendering methods and outperform them with promising subjective and objective results on a challenging dataset.