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Tobias Höllerer
Researcher at University of California, Santa Barbara
Publications - 265
Citations - 10024
Tobias Höllerer is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Augmented reality & User interface. The author has an hindex of 48, co-authored 254 publications receiving 8972 citations. Previous affiliations of Tobias Höllerer include University of California & University of Trier.
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Proceedings ArticleDOI
Extreme field-of-view for head-mounted displays
TL;DR: Novel optics and head-mounted display prototypes, which have the widest reported field-of-view (FOV), and which can cover the full human FOV or even beyond, are presented.
Proceedings ArticleDOI
Fast and scalable keypoint recognition and image retrieval using binary codes
Jonathan Ventura,Tobias Höllerer +1 more
TL;DR: Tests using image datasets with perspective distortion show the method to enable fast keypoint recognition and image retrieval with a small code size, and point towards potential applications for scalable visual SLAM on mobile phones.
Proceedings Article
2D-3D Co-segmentation for AR-based Remote Collaboration.
TL;DR: An algorithm is presented to segment the selected object, including its occluded surfaces, such that the 2D selection can be appropriately interpreted in 3D and rendered as a useful AR annotation even when the local user moves and significantly changes the viewpoint.
Proceedings ArticleDOI
PPV: Pixel-Point-Volume Segmentation for Object Referencing in Collaborative Augmented Reality
TL;DR: A novel segmentation algorithm that utilizes both 2D and 3D scene cues, structured into a three-layer graph of pixels, 3D points, and volumes (supervoxels), solved via standard graph cut algorithms is proposed.
Proceedings ArticleDOI
A Study of Situated Product Recommendations in Augmented Reality
TL;DR: This work explores the effects of a recommender system for online shopping which allows customers to view personalized product recommendations in the physical spaces where they might be used and describes results of a 2x3 condition exploratory study in which recommendation quality was varied across 3 user interface types.