Inattentional Blindness for Redirected Walking Using Dynamic Foveated Rendering

Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation

Inattentional Blindness for Redirected Walking Using Dynamic Foveated Rendering

Redirected walking is a Virtual Reality(VR) locomotion technique which enables users to navigate virtual environments (VEs) that are spatially larger than the available physical tracked space. In this work we present a novel technique for redirected walking in VR based on the psychological phenomenon of inattentional blindness. Based on the user's visual fixation points we divide the user's view into zones. Spatially-varying rotations are applied according to the zone's importance and are rendered using foveated rendering. Our technique is real-time and applicable to small and large physical spaces. Furthermore, the proposed technique does not require the use of stimulated saccades but rather takes advantage of naturally occurring saccades and blinks for a complete refresh of the framebuffer. We performed extensive testing and present the analysis of the results of three user studies conducted for the evaluation

Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset

Recent advancements in deep learning and computer vision have led to widespread use of deep neural networks to extract building footprints from remote-sensing imagery. The success of such methods relies on the availability of large databases of high-resolution remote sensing images with high-quality annotations. The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks. This dataset consists of $\sim\$280k training images and $\sim\$60k testing images, with polygonal building annotations for all images. However, issues such as low-quality and incorrect annotations, extensive duplication of image samples, and data leakage significantly reduce the utility of deep neural networks trained on the dataset. Therefore, it is an imperative pre-condition to adopt a data validation pipeline that evaluates the quality of the dataset prior to its use. To this end, we propose a drop-in pipeline that employs perceptual hashing techniques for efficient de-duplication of the dataset and identification of instances of data leakage between training and testing splits. In our experiments, we demonstrate that nearly 250k($\sim\$90%) images in the training split were identical. Moreover, our analysis on the validation split demonstrates that roughly 56k of the 60k images also appear in the training split, resulting in a data leakage of 93%. The source code used for the analysis and de-duplication of the CrowdAI Mapping Challenge dataset is publicly available at https://github.com/yeshwanth95/CrowdAI_Hash_and_search .Comment: 9 pages, 2 figure

Effectiveness of an Immersive Virtual Environment (CAVE) for Teaching Pedestrian Crossing to Children with PDD-NOS

Children with Autism Spectrum Disorders (ASD) exhibit a range of developmental disabilities, with mild to severe effects in social interaction and communication. Children with PDD-NOS, Autism and co-existing conditions are facing enormous challenges in their lives, dealing with their difficulties in sensory perception, repetitive behaviors and interests. These challenges result in them being less independent or not independent at all. Part of becoming independent involves being able to function in real world settings, settings that are not controlled. Pedestrian crossings fall under this category: as children (and later as adults) they have to learn to cross roads safely. In this paper, we report on a study we carried out with 6 children with PDD-NOS over a period of four (4) days using a VR CAVE virtual environment to teach them how to safely cross at a pedestrian crossing. Results indicated that most children were able to achieve the desired goal of learning the task, which was verified in the end of the 4-day period by having them cross a real pedestrian crossing (albeit with their parent/educator discretely next to them for safety reasons)

Development and integration of digital technologies addressed to raise awareness and access to European underwater cultural heritage. An overview of the H2020 i-MARECULTURE project

The Underwater Cultural Heritage (UCH) represents a vast historical and scientific resource that, often, is not accessible to the general public due the environment and depth where it is located. Digital technologies (Virtual Museums, Virtual Guides and Virtual Reconstruction of Cultural Heritage) provide a unique opportunity for digital accessibility to both scholars and general public, interested in having a better grasp of underwater sites and maritime archaeology. This paper presents the architecture and the first results of the Horizon 2020 iMARECULTURE (Advanced VR, iMmersive Serious Games and Augmented REality as Tools to Raise Awareness and Access to European Underwater CULTURal heritage) project that aims to develop and integrate digital technologies for supporting the wide public in acquiring knowledge about UCH. A Virtual Reality (VR) system will be developed to allow users to visit the underwater sites through the use of Head Mounted Displays (HMDs) or digital holographic screens. Two serious games will be implemented for supporting the understanding of the ancient Mediterranean seafaring and the underwater archaeological excavations. An Augmented Reality (AR) system based on an underwater tablet will be developed to serve as virtual guide for divers that visit the underwater archaeological sites

3D reconstruction of urban areas

Abstract Virtual representations of real world areas are increasingly being employed in a variety of different applications such as urban planning, personnel training, simulations, etc. Despite the increasing demand for such realistic 3D representations, it still remains a very hard and often manual process. In this paper, we address the problem of creating photorealistic 3D scene models for large-scale areas and present a complete system. The proposed system comprises of two main components: (1) A reconstruction pipeline which employs a fully automatic technique for extracting and producing high-fidelity geometric models directly from Light Detection and Ranging (LiDAR) data and (2) A flexible texture blending technique for generating high-quality photorealistic textures by fusing information from multiple optical sensor resources. The result is a photorealistic 3D representation of large-scale areas(city-size) of the real-world. We have tested the proposed system extensively with many city-size datasets which confirms the validity and robustness of the approach. The reported results verify that the system is a consistent work flow that allows non-expert and non-artists to rapidly fuse aerial LiDAR and imagery to construct photorealistic 3D scene models