Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens

Rusu, Marius und Mayer, Sven (2023): Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens. CHI '23: CHI Conference on Human Factors in Computing Systems, Hamburg Germany, April 23 - 28, 2023. Schmidt, Albrecht; Väänänen, Kaisa; Goyal, Tesh; Kristensson, Per Ola; Peters, Anicia; Mueller, Stefanie; Williamson, Julie R. und Wilson, Max L. (Hrsg.): In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 199 New York, NY ,United States: Association for Computing Machinery.

Abstract

Over the last few years, we have seen many approaches using tangibles to address the limited expressiveness of touchscreens. Mainstream tangible detection uses fiducial markers embedded in the tangibles. However, the coarse sensor size of capacitive touchscreens makes tangibles bulky, limiting their usefulness. We propose a novel deep-learning super-resolution network to facilitate fiducial tangibles on capacitive touchscreens better. In detail, our network super-resolves the markers enabling off-the-shelf detection algorithms to track tangibles reliably. Our network generalizes to unseen marker sets, such as AprilTag, ArUco, and ARToolKit. Therefore, we are not limited to a fixed number of distinguishable objects and do not require data collection and network training for new fiducial markers. With extensive evaluation, including real-world users and five showcases, we demonstrate the applicability of our open-source approach on commodity mobile devices and further highlight the potential of tangibles on capacitive touchscreens.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-1-4503-9421-5
• Ort: New York, NY ,United States
• Sprache: Englisch