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

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

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Casual 3D photography

We present an algorithm that enables casual 3D photography. Given a set of input photos captured with a hand-held cell phone or DSLR camera, our algorithm reconstructs a 3D photo, a central panoramic, textured, normal mapped, multi-layered geometric mesh representation. 3D photos can be stored compactly and are optimized for being rendered from viewpoints that are near the capture viewpoints. They can be rendered using a standard rasterization pipeline to produce perspective views with motion parallax. When viewed in VR, 3D photos provide geometrically consistent views for both eyes. Our geometric representation also allows interacting with the scene using 3D geometry-aware effects, such as adding new objects to the scene and artistic lighting effects. Our 3D photo reconstruction algorithm starts with a standard structure from motion and multi-view stereo reconstruction of the scene. The dense stereo reconstruction is made robust to the imperfect capture conditions using a novel near envelope cost volume prior that discards erroneous near depth hypotheses. We propose a novel parallax-tolerant stitching algorithm that warps the depth maps into the central panorama and stitches two color-and-depth panoramas for the front and back scene surfaces. The two panoramas are fused into a single non-redundant, well-connected geometric mesh. We provide videos demonstrating users interactively viewing and manipulating our 3D photos

Methods for reducing visual discomfort in stereoscopic 3D: A review

This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”

Viewpoint-Free Photography for Virtual Reality

Viewpoint-free photography, i.e., interactively controlling the viewpoint of a photograph after capture, is a standing challenge. In this thesis, we investigate algorithms to enable viewpoint-free photography for virtual reality (VR) from casual capture, i.e., from footage easily captured with consumer cameras. We build on an extensive body of work in image-based rendering (IBR). Given images of an object or scene, IBR methods aim to predict the appearance of an image taken from a novel perspective. Most IBR methods focus on full or near-interpolation, where the output viewpoints either lie directly between captured images, or nearby. These methods are not suitable for VR, where the user has significant range of motion and can look in all directions. Thus, it is essential to create viewpoint-free photos with a wide field-of-view and sufficient positional freedom to cover the range of motion a user might experience in VR. We focus on two VR experiences: 1) Seated VR experiences, where the user can lean in different directions. This simplifies the problem, as the scene is only observed from a small range of viewpoints. Thus, we focus on easy capture, showing how to turn panorama-style capture into 3D photos, a simple representation for viewpoint-free photos, and also how to speed up processing so users can see the final result on-site. 2) Room-scale VR experiences, where the user can explore vastly different perspectives. This is challenging: More input footage is needed, maintaining real-time display rates becomes difficult, view-dependent appearance and object backsides need to be modelled, all while preventing noticeable mistakes. We address these challenges by: (1) creating refined geometry for each input photograph, (2) using a fast tiled rendering algorithm to achieve real-time display rates, and (3) using a convolutional neural network to hide visual mistakes during compositing. Overall, we provide evidence that viewpoint-free photography is feasible from casual capture. We thoroughly compare with the state-of-the-art, showing that our methods achieve both a numerical improvement and a clear increase in visual quality for both seated and room-scale VR experiences

Localisation and tracking of stationary users for extended reality

In this thesis, we investigate the topics of localisation and tracking in the context of Extended Reality. In many on-site or outdoor Augmented Reality (AR) applications, users are standing or sitting in one place and performing mostly rotational movements, i.e. stationary. This type of stationary motion also occurs in Virtual Reality (VR) applications such as panorama capture by moving a camera in a circle. Both applications require us to track the motion of a camera in potentially very large and open environments. State-of-the-art methods such as Structure-from-Motion (SfM), and Simultaneous Localisation and Mapping (SLAM), tend to rely on scene reconstruction from significant translational motion in order to compute camera positions. This can often lead to failure in application scenarios such as tracking for seated sport spectators, or stereo panorama capture where the translational movement is small compared to the scale of the environment. To begin with, we investigate the topic of localisation as it is key to providing global context for many stationary applications. To achieve this, we capture our own datasets in a variety of large open spaces including two sports stadia. We then develop and investigate these techniques in the context of these sports stadia using a variety of state-of-the-art localisation approaches. We cover geometry-based methods to handle dynamic aspects of a stadium environment, as well as appearance-based methods, and compare them to a state-of-the-art SfM system to identify the most applicable methods for server-based and on-device localisation. Recent work in SfM has shown that the type of stationary motion that we target can be reliably estimated by applying spherical constraints to the pose estimation. In this thesis, we extend these concepts into a real-time keyframe-based SLAM system for the purposes of AR, and develop a unique data structure for simplifying keyframe selection. We show that our constrained approach can track more robustly in these challenging stationary scenarios compared to state-of-the-art SLAM through both synthetic and real-data tests. In the application of capturing stereo panoramas for VR, this thesis demonstrates the unsuitability of standard SfM techniques for reconstructing these circular videos. We apply and extend recent research in spherically constrained SfM to creating stereo panoramas and compare this with state-of-the-art general SfM in a technical evaluation. With a user study, we show that the motion requirements of our SfM approach are similar to the natural motion of users, and that a constrained SfM approach is sufficient for providing stereoscopic effects when viewing the panoramas in VR