Stereoscopic image stitching with rectangular boundaries

This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience

Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery

Minimally invasive surgery is playing an increasingly important role for patient care. Whilst its direct patient benefit in terms of reduced trauma, improved recovery and shortened hospitalisation has been well established, there is a sustained need for improved training of the existing procedures and the development of new smart instruments to tackle the issue of visualisation, ergonomic control, haptic and tactile feedback. For endoscopic intervention, the small field of view in the presence of a complex anatomy can easily introduce disorientation to the operator as the tortuous access pathway is not always easy to predict and control with standard endoscopes. Effective training through simulation devices, based on either virtual reality or mixed-reality simulators, can help to improve the spatial awareness, consistency and safety of these procedures. This thesis examines the use of endoscopic videos for both simulation and navigation purposes. More specifically, it addresses the challenging problem of how to build high-fidelity subject-specific simulation environments for improved training and skills assessment. Issues related to mesh parameterisation and texture blending are investigated. With the maturity of computer vision in terms of both 3D shape reconstruction and localisation and mapping, vision-based techniques have enjoyed significant interest in recent years for surgical navigation. The thesis also tackles the problem of how to use vision-based techniques for providing a detailed 3D map and dynamically expanded field of view to improve spatial awareness and avoid operator disorientation. The key advantage of this approach is that it does not require additional hardware, and thus introduces minimal interference to the existing surgical workflow. The derived 3D map can be effectively integrated with pre-operative data, allowing both global and local 3D navigation by taking into account tissue structural and appearance changes. Both simulation and laboratory-based experiments are conducted throughout this research to assess the practical value of the method proposed

Remote Visual Observation of Real Places Through Virtual Reality Headsets

Virtual Reality has always represented a fascinating yet powerful opportunity that has attracted studies and technology developments, especially since the latest release on the market of powerful high-resolution and wide field-of-view VR headsets. While the great potential of such VR systems is common and accepted knowledge, issues remain related to how to design systems and setups capable of fully exploiting the latest hardware advances. The aim of the proposed research is to study and understand how to increase the perceived level of realism and sense of presence when remotely observing real places through VR headset displays. Hence, to produce a set of guidelines that give directions to system designers about how to optimize the display-camera setup to enhance performance, focusing on remote visual observation of real places. The outcome of this investigation represents unique knowledge that is believed to be very beneficial for better VR headset designs towards improved remote observation systems. To achieve the proposed goal, this thesis presents a thorough investigation of existing literature and previous researches, which is carried out systematically to identify the most important factors ruling realism, depth perception, comfort, and sense of presence in VR headset observation. Once identified, these factors are further discussed and assessed through a series of experiments and usability studies, based on a predefined set of research questions. More specifically, the role of familiarity with the observed place, the role of the environment characteristics shown to the viewer, and the role of the display used for the remote observation of the virtual environment are further investigated. To gain more insights, two usability studies are proposed with the aim of defining guidelines and best practices. The main outcomes from the two studies demonstrate that test users can experience an enhanced realistic observation when natural features, higher resolution displays, natural illumination, and high image contrast are used in Mobile VR. In terms of comfort, simple scene layouts and relaxing environments are considered ideal to reduce visual fatigue and eye strain. Furthermore, sense of presence increases when observed environments induce strong emotions, and depth perception improves in VR when several monocular cues such as lights and shadows are combined with binocular depth cues. Based on these results, this investigation then presents a focused evaluation on the outcomes and introduces an innovative eye-adapted High Dynamic Range (HDR) approach, which the author believes to be of great improvement in the context of remote observation when combined with eye-tracked VR headsets. Within this purpose, a third user study is proposed to compare static HDR and eye-adapted HDR observation in VR, to assess that the latter can improve realism, depth perception, sense of presence, and in certain cases even comfort. Results from this last study confirmed the author expectations, proving that eye-adapted HDR and eye tracking should be used to achieve best visual performances for remote observation in modern VR systems

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Department of Biomedical EngineeringImage stitching is a well-known method to make panoramic image which has a wide field-of-view and high resolution. It has been used in various fields such as digital map, gigapixel imaging, and 360-degree camera. However, commercial stitching tools often fail, require a lot of processing time, and only work on certain images. The problems of existing tools are mainly caused by trying to stitch the wrong image pair. To overcome these problems, it is important to select suitable image pair for stitching in advance. Nevertheless, there are no universal standards to judge the good image pairs. Moreover, the derived stitching algorithms cannot be compatible with each other because they conform to their own available criteria. Here, we present universal stitching parameters and their conditions for selecting good image pairs. The proposed stitching parameters can be easily calculated through analysis of corresponding features and homography, which are basic elements in feature-based image stitching algorithm. In order to specify the conditions of the stitching parameters, we devised a new method to calculate stitching accuracy for qualifying stitching results into 3 classesgood, bad, and fail. With the classed stitching results, the values of the stitching parameters could be checked how they differ in each class. Through experiments with large datasets, the most valid parameter for each class is identified as filtering level which is calculated in corresponding feature analysis. In addition, supplemental experiments were conducted with various datasets to demonstrate the validity of the filtering level. As a result of our study, universal stitching parameters can judge the success of stitching, so that it is possible to prevent stitching errors through parameter verification test in advance. This paper can greatly contribute to guide for creating high performance and high efficiency stitching software by applying the proposed stitching conditions.ope

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