Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Sk Aziz Ali
Rashed Al Koutayni
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Tewodros Amberbir Habtegebrial
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Mina Ameli
Nareg Minaskan Karabid
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Murad Almadani
Ahmet Firintepe
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
HMDPose: A large-scale trinocular IR Augmented Reality Glasses Pose Dataset
HMDPose: A large-scale trinocular IR Augmented Reality Glasses Pose Dataset
Proc. of. ACM Symposium on Virtual Reality Software and Technology (VRST-2020) ACM 2020 .
- Abstract:
- Augmented Reality Glasses usually implement an Inside-Out tracking. In case of a driving scenario or glasses with less computation capabilities, an Outside-In tracking approach is required. However, to the best of our knowledge, no public datasets exist that collects images of users wearing AR glasses. To address this problem, we present HMDPose, an infrared trinocular dataset of four different AR Head-mounted displays captured in a car. It contains sequences of 14 subjects captured by three different cameras running at 60 FPS each, adding up to more than 3,000,000 labeled images in total. We provide a ground truth 6DoF-pose, captured by a marker-based tracker with submillimeter accuracy. We make this dataset publicly available for non-profit, academic use and non-commercial benchmarking.