Image-Based Scene Representations for Head-Motion Parallax in 360° Panoramas

Creation and delivery of “RealVR” experiences essentially consists of the following four main steps: capture, processing, representation and rendering. In this chapter, we present, compare, and discuss two recent end-to-end approaches, Parallax360 by Luo et al. [9] and MegaParallax by Bertel et al. [3]. Both propose complete pipelines for RealVR content generation and novel-view synthesis with head-motion parallax for 360° environments.Parallax360 uses a robotic arm for capturing thousands of input views on the surface of a sphere. Based on precomputed disparity motion fields and pairwise optical flow, novel viewpoints are synthesized on the fly using flow-based blending of the nearest two to three input views which provides compelling head-motion parallax.MegaParallax proposes a pipeline for RealVR content generation and rendering that emphasizes casual, hand-held capturing. The approach introduces view-dependent flow-based blending to enable novel-view synthesis with head-motion parallax within a viewing area determined by the field of view of the input cameras and the capturing radius.We describe both methods and discuss their similarities and differences in corresponding steps in the RealVR pipeline and show selected results. The chapter ends by discussing advantages and disadvantages as well as outlining the most important limitations and future work.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 66599

OmniPhotos: Casual 360° VR Photography

Virtual reality headsets are becoming increasingly popular, yet it remains difficult for casual users to capture immersive 360° VR panoramas. State-of-the-art approaches require capture times of usually far more than a minute and are often limited in their supported range of head motion. We introduce OmniPhotos, a novel approach for quickly and casually capturing high-quality 360° panoramas with motion parallax. Our approach requires a single sweep with a consumer 360° video camera as input, which takes less than 3 seconds to capture with a rotating selfie stick or 10 seconds handheld. This is the fastest capture time for any VR photography approach supporting motion parallax by an order of magnitude. We improve the visual rendering quality of our OmniPhotos by alleviating vertical distortion using a novel deformable proxy geometry, which we fit to a sparse 3D reconstruction of captured scenes. In addition, the 360° input views significantly expand the available viewing area, and thus the range of motion, compared to previous approaches. We have captured more than 50 OmniPhotos and show video results for a large variety of scenes.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 66599

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

We introduce a method to convert stereo 360{\deg} (omnidirectional stereo) imagery into a layered, multi-sphere image representation for six degree-of-freedom (6DoF) rendering. Stereo 360{\deg} imagery can be captured from multi-camera systems for virtual reality (VR), but lacks motion parallax and correct-in-all-directions disparity cues. Together, these can quickly lead to VR sickness when viewing content. One solution is to try and generate a format suitable for 6DoF rendering, such as by estimating depth. However, this raises questions as to how to handle disoccluded regions in dynamic scenes. Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR. This significantly improves comfort for the viewer, and can be inferred and rendered in real time on modern GPU hardware. Together, these move towards making VR video a more comfortable immersive medium.Comment: 25 pages, 13 figures, Published at European Conference on Computer Vision (ECCV 2020), Project Page: http://visual.cs.brown.edu/matryodshk

3D Scene Geometry Estimation from 360∘^\circ Imagery: A Survey

This paper provides a comprehensive survey on pioneer and state-of-the-art 3D scene geometry estimation methodologies based on single, two, or multiple images captured under the omnidirectional optics. We first revisit the basic concepts of the spherical camera model, and review the most common acquisition technologies and representation formats suitable for omnidirectional (also called 360$^\circ$, spherical or panoramic) images and videos. We then survey monocular layout and depth inference approaches, highlighting the recent advances in learning-based solutions suited for spherical data. The classical stereo matching is then revised on the spherical domain, where methodologies for detecting and describing sparse and dense features become crucial. The stereo matching concepts are then extrapolated for multiple view camera setups, categorizing them among light fields, multi-view stereo, and structure from motion (or visual simultaneous localization and mapping). We also compile and discuss commonly adopted datasets and figures of merit indicated for each purpose and list recent results for completeness. We conclude this paper by pointing out current and future trends.Comment: Published in ACM Computing Survey

Parallax Motion Effect Generation Through Instance Segmentation And Depth Estimation

Stereo vision is a growing topic in computer vision due to the innumerable opportunities and applications this technology offers for the development of modern solutions, such as virtual and augmented reality applications. To enhance the user's experience in three-dimensional virtual environments, the motion parallax estimation is a promising technique to achieve this objective. In this paper, we propose an algorithm for generating parallax motion effects from a single image, taking advantage of state-of-the-art instance segmentation and depth estimation approaches. This work also presents a comparison against such algorithms to investigate the trade-off between efficiency and quality of the parallax motion effects, taking into consideration a multi-task learning network capable of estimating instance segmentation and depth estimation at once. Experimental results and visual quality assessment indicate that the PyD-Net network (depth estimation) combined with Mask R-CNN or FBNet networks (instance segmentation) can produce parallax motion effects with good visual quality.Comment: 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirate

Comparing of radial and tangencial geometric for cylindric panorama

Cameras generally have a field of view only large enough to capture a portion of their surroundings. The goal of immersion is to replace many of your senses with virtual ones, so that the virtual environment will feel as real as possible. Panoramic cameras are used to capture the entire 360°view, also known as panoramic images.Virtual reality makes use of these panoramic images to provide a more immersive experience compared to seeing images on a 2D screen. This thesis, which is in the field of Computer vision, focuses on establishing a multi-camera geometry to generate a cylindrical panorama image and successfully implementing it with the cheapest cameras possible. The specific goal of this project is to propose the cameras geometry which will decrease artifact problems related to parallax in the panorama image. We present a new approach of cylindrical panoramic images from multiple cameras which its setup has cameras placed evenly around a circle. Instead of looking outward, which is the traditional ”radial” configuration, we propose to make the optical axes tangent to the camera circle, a ”tangential” configuration. Beside an analysis and comparison of radial and tangential geometries, we provide an experimental setup with real panoramas obtained in realistic conditionsLes caméras ont généralement un champ de vision à peine assez grand pour capturer partie de leur environnement. L’objectif de l’immersion est de remplacer virtuellement un grand nombre de sens, de sorte que l’environnement virtuel soit perçu comme le plus réel possible. Une caméra panoramique est utilisée pour capturer l’ensemble d’une vue 360°, également connue sous le nom d’image panoramique. La réalité virtuelle fait usage de ces images panoramiques pour fournir une expérience plus immersive par rapport aux images sur un écran 2D. Cette thèse, qui est dans le domaine de la vision par ordinateur, s’intéresse à la création d’une géométrie multi-caméras pour générer une image cylindrique panoramique et vise une mise en œuvre avec les caméras moins chères possibles. L’objectif spécifique de ce projet est de proposer une géométrie de caméra qui va diminuer au maximum les problèmes d’artefacts liés au parallaxe présent dans l’image panoramique. Nous présentons une nouvelle approche de capture des images panoramiques cylindriques à partir de plusieurs caméras disposées uniformément autour d’un cercle. Au lieu de regarder vers l’extérieur, ce qui est la configuration traditionnelle ”radiale”, nous proposons de rendre les axes optiques tangents au cercle des caméras, une configuration ”tangentielle”. Outre une analyse et la comparaison des géométries radiales et tangentielles, nous fournissons un montage expérimental avec de vrais panoramas obtenus dans des conditions réaliste

Capture, Reconstruction, and Representation of the Visual Real World for Virtual Reality

We provide an overview of the concerns, current practice, and limitations for capturing, reconstructing, and representing the real world visually within virtual reality. Given that our goals are to capture, transmit, and depict complex real-world phenomena to humans, these challenges cover the opto-electro-mechanical, computational, informational, and perceptual fields. Practically producing a system for real-world VR capture requires navigating a complex design space and pushing the state of the art in each of these areas. As such, we outline several promising directions for future work to improve the quality and flexibility of real-world VR capture systems

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality