HMDPose: A large-scale trinocular IR Augmented Reality Glasses Pose Dataset

HMDPose: A large-scale trinocular IR Augmented Reality Glasses Pose Dataset
Ahmet Firintepe, Alain Pagani, Didier Stricker
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.