Adaptive User Perspective Rendering for Handheld Augmented Reality

Handheld Augmented Reality commonly implements some variant of magic lens rendering, which turns only a fraction of the user's real environment into AR while the rest of the environment remains unaffected. Since handheld AR devices are commonly equipped with video see-through capabilities, AR magic lens applications often suffer from spatial distortions, because the AR environment is presented from the perspective of the camera of the mobile device. Recent approaches counteract this distortion based on estimations of the user's head position, rendering the scene from the user's perspective. To this end, approaches usually apply face-tracking algorithms on the front camera of the mobile device. However, this demands high computational resources and therefore commonly affects the performance of the application beyond the already high computational load of AR applications. In this paper, we present a method to reduce the computational demands for user perspective rendering by applying lightweight optical flow tracking and an estimation of the user's motion before head tracking is started. We demonstrate the suitability of our approach for computationally limited mobile devices and we compare it to device perspective rendering, to head tracked user perspective rendering, as well as to fixed point of view user perspective rendering

enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets

Jointly analyzing biological pathway maps and experimental data is critical for understanding how biological processes work in different conditions and why different samples exhibit certain characteristics. This joint analysis, however, poses a significant challenge for visualization. Current techniques are either well suited to visualize large amounts of pathway node attributes, or to represent the topology of the pathway well, but do not accomplish both at the same time. To address this we introduce enRoute, a technique that enables analysts to specify a path of interest in a pathway, extract this path into a separate, linked view, and show detailed experimental data associated with the nodes of this extracted path right next to it. This juxtaposition of the extracted path and the experimental data allows analysts to simultaneously investigate large amounts of potentially heterogeneous data, thereby solving the problem of joint analysis of topology and node attributes. As this approach does not modify the layout of pathway maps, it is compatible with arbitrary graph layouts, including those of hand-crafted, image-based pathway maps. We demonstrate the technique in context of pathways from the KEGG and the Wikipathways databases. We apply experimental data from two public databases, the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) that both contain a wide variety of genomic datasets for a large number of samples. In addition, we make use of a smaller dataset of hepatocellular carcinoma and common xenograft models. To verify the utility of enRoute, domain experts conducted two case studies where they explore data from the CCLE and the hepatocellular carcinoma datasets in the context of relevant pathways

Object Pose Detection to Enable 3D Interaction from 2D Equirectangular Images in Mixed Reality Educational Settings

In this paper, we address the challenge of estimating the 6DoF pose of objects in 2D equirectangular images. This solution allows the transition to the objects’ 3D model from their current pose. In particular, it finds application in the educational use of 360° videos, where it enhances the learning experience of students by making it more engaging and immersive due to the possible interaction with 3D virtual models. We developed a general approach usable for any object and shape. The only requirement is to have an accurate CAD model, even without textures of the item, whose pose must be estimated. The developed pipeline has two main steps: vehicle segmentation from the image background and estimation of the vehicle pose. To accomplish the first task, we used deep learning methods, while for the second, we developed a 360° camera simulator in Unity to generate synthetic equirectangular images used for comparison. We conducted our tests using a miniature truck model whose CAD was at our disposal. The developed algorithm was tested using a metrological analysis applied to real data. The results showed a mean difference of 1.5° with a standard deviation of 1° from the ground truth data for rotations, and 1.4 cm with a standard deviation of 1.5 cm for translations over a research range of ±20° and ±20 cm, respectively

Omnidirectional camera pose estimation and projective texture mapping for photorealistic 3D virtual reality experiences

Modern applications in virtual reality require a high level of fruition of the environment as if it was real. In applications that have to deal with real scenarios, it is important to acquire both its three-dimensional (3D) structure and details to enable the users to achieve good immersive experiences. The purpose of this paper is to illustrate a method to obtain a mesh with high quality texture combining a raw 3D mesh model of the environment and 360 ° images. The main outcome is a mesh with a high level of photorealistic details. This enables both a good depth perception thanks to the mesh model and high visualization quality thanks to the 2D resolution of modern omnidirectional cameras. The fundamental step to reach this goal is the correct alignment between the 360 ° camera and the 3D mesh model. For this reason, we propose a method that embodies two steps: 1) find the 360 ° cameras pose within the current 3D environment; 2) project the high-quality 360 ° image on top of the mesh. After the method description, we outline its validation in two virtual reality scenarios, a mine and city environment, respectively, which allows us to compare the achieved results with the ground truth.</p

Entourage: Visualizing Relationships between Biological Pathways using Contextual Subsets

Biological pathway maps are highly relevant tools for many tasks in molecular biology. They reduce the complexity of the overall biological network by partitioning it into smaller manageable parts. While this reduction of complexity is their biggest strength, it is, at the same time, their biggest weakness. By removing what is deemed not important for the primary function of the pathway, biologists lose the ability to follow and understand cross-talks between pathways. Considering these cross-talks is, however, critical in many analysis scenarios, such as judging effects of drugs. In this paper we introduce Entourage, a novel visualization technique that provides contextual information lost due to the artificial partitioning of the biological network, but at the same time limits the presented information to what is relevant to the analyst’s task. We use one pathway map as the focus of an analysis and allow a larger set of contextual pathways. For these context pathways we only show the contextual subsets, i.e., the parts of the graph that are relevant to a selection. Entourage suggests related pathways based on similarities and highlights parts of a pathway that are interesting in terms of mapped experimental data. We visualize interdependencies between pathways using stubs of visual links, which we found effective yet not obtrusive. By combining this approach with visualization of experimental data, we can provide domain experts with a highly valuable tool. We demonstrate the utility of Entourage with case studies conducted with a biochemist who researches the effects of drugs on pathways. We show that the technique is well suited to investigate interdependencies between pathways and to analyze, understand, and predict the effect that drugs have on different cell types.Engineering and Applied Science

Designing for Mixed Reality Urban Exploration

This paper introduces a design framework for mixed reality urban exploration (MRUE), based on a concrete implementation in a historical city. The framework integrates different modalities, such as virtual reality (VR), augmented reality (AR), and haptics-audio interfaces, as well as advanced features such as personalized recommendations, social exploration, and itinerary management. It permits to address a number of concerns regarding information overload, safety, and quality of the experience, which are not sufficiently tackled in traditional non-integrated approaches. This study presents an integrated mobile platform built on top of this framework and reflects on the lessons learned.Peer reviewe

Learning Lightprobes for Mixed Reality Illumination

This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation by convolutional neural network (CNN) methods. For easy adaptation and deployment of the system, we train the CNN using purely synthetic images and apply them to real image data. To keep the pipeline accurate and efficient, we propose to fuse the light estimation results from multiple CNN instances, and we show an approach for caching estimates over time. For optimal performance, we furthermore explore multiple strategies for the CNN training. Experimental results show that the proposed method yields highly accurate estimates for photo-realistic augmentations