Dr. Muhammad Zeshan Afzal

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Iuliia Brishtel

Mahdi Chamseddine

Steve Dias da Cruz

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst

Kamalveerkaur Garewal

Christiano Couto Gava

Leif Eric Goebel

Tewodros Amberbir Habtegebrial

Jigyasa Singh Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Mohammad Minouei

Manthan Pancholi

Engr. Kumail Raza

Dr. Nadia Robertini

María Alejandra Sánchez Marín

Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Lukas Stefan Staecker

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão

A Comparison of Single and Multi-View IR image-based AR Glasses Pose Estimation Approaches

A Comparison of Single and Multi-View IR image-based AR Glasses Pose Estimation Approaches
Ahmet Firintepe, Alain Pagani, Didier Stricker
Proceedings of the IEEE Virtual Reality conference. IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) (IEEEVR-2021) IEEE 2021 .

Abstract:: In this paper, we present a study on single and multi-view image-based AR glasses pose estimation with two novel methods. The first approach is named GlassPose and is a VGG-based network. The second approach GlassPoseRN is based on ResNet18. We train and evaluate the two custom developed glasses pose estimation networks with one, two and three input images on the HMDPose dataset. We achieve errors as low as 0.10 degrees and 0.90mm on average on all axes for orientation and translation. For both networks, we observe minimal improvements in position estimation with more input views.

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