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

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

Efficient Distance Accuracy Estimation Of Real-World Environments In Virtual Reality Head-Mounted Displays

Virtual reality (VR) is a very promising technology with many compelling industrial applications. As many advancements have been made recently to deploy and use VR technology in virtual environments, they are still less mature to be used to render real environments. The current VR systems settings, which are developed for virtual environments rendering, fail to adequately address the challenges of capturing and displaying real-world virtual reality that these systems entail. Before these systems can be used in real life settings, their performance needs to be investigated, more specifically, depth perception and how distances to objects in the rendered scenes are estimated. The perceived depth is influenced by Head Mounted Displays (HMD) that inevitability decrease the virtual content’s depth perception. Distances are consistently underestimated in virtual environments (VEs) compared to the real world. The reason behind this underestimation is still not understood. This thesis investigates another version of this kind of system, that to the best of authors knowledge has not been explored by any previous research. Previous research used a computer-generated scene. This work is examining distance estimation in real environments rendered to Head-Mounted Displays, where distance estimations is among the most challenging issues that are still investigated and not fully understood.This thesis introduces a dual-camera video feed system through a virtual reality head mounted display with two models: a video-based and a static photo-based model, in which, the purpose is to explore whether the misjudgment of distances in HMDs could be due to a lack of realism, or not, with the use of a real-world scene rendering system. Distance judgments performance in the real world and these two evaluated VE models were compared using protocols already proven to accurately measure real-world distance estimations. An improved model based on enhancing the field of view (FOV) of the displayed scenes to improve distance judgements when displaying real-world VR content to HMDs was developed; allowing to mitigate the limited FOV, which is among the first potential causes of distance underestimation, specially, the mismatch of FOV between the camera and the HMD field of views. The proposed model is using a set of two cameras to generate the video instead of hundreds of input cameras or tens of cameras mounted on a circular rig as previous works from the literature. First Results from the first implementation of this system found that when the model was rendered as static photo-based, the underestimation was less as compared with the live video feed rendering. The video-based (real + HMD) model and the static photo-based (real + photo + HMD) model averaged 80.2% of the actual distance, and 81.4% respectively compared to the Real-World estimations that averaged 92.4%. The improved developed approach (Real + HMD + FOV) was compared to these two models and showed an improvement of 11%, increasing the estimation accuracy from 80% to 91% and reducing the estimation error from 1.29% to 0.56%. This thesis results present strong evidence of the need for novel distance estimation improvements methods for real world VR content systems and provides effective initial work towards this goal

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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

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