Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are no...
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ISBN:
(数字)9798331536459
ISBN:
(纸本)9798331536466
Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.
Tile-based locomotion (TBL) is a popular locomotion technique for computer, console, and board games. However, despite its simplicity and unconventional movement, the transfer of TBL to virtual reality (VR) as a game ...
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Computational notebooks, widely used for ad-hoc analysis and often shared with others, can be difficult to understand because the standard linear layout is not optimized for reading. In particular, related text, code,...
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Environmentally friendly and hygromorphic actuators have gained increasing attention for energy harvesting, field robotics, seeding and biodegradable active structures and sensors. While recent works have used hygromo...
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Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in ...
Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy.
The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using re...
The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.
This study examines how anecdotal stories from friends, peers, and online sources influence non-experts' perceptions and behaviors toward smart home IoT devices. We surveyed 263 participants, collecting narratives...
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Artificial intelligence (AI) applications in healthcare offer the promise of improved decision making for clinicians, and better healthcare outcomes for patients. While technical AI advances in healthcare showcase imp...
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Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual sc...
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Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its enormous potential for virtual reality (VR) applications, there is currently little robust integration of NeRF into typical VR systems available for research and benchmarking in the VR community. In this poster paper, we present an extension to instant neural graphics primitives and bring stereoscopic, high-resolution, low-latency, 6-DoF NeRF rendering to the Unity game engine for immersive VR applications. 1 1 Link to the repository: https://***/uhhhci/immersive-ngp
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