Prof. Dr. Didier Stricker

Dr. Alain Pagani

Nicole Stephanie Buhlinger

Keonna Cunningham

Jason Raphael Rambach

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bertram Taetz

Rashed Al Koutayni

Jilliam Maria Diaz Barros

Mahdi Chamseddine

Steve Dias da Cruz

Christiano Couto Gava

Tewodros Amberbir Habtegebrial

Jigyasa Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Manthan Pancholi

María Alejandra Sánchez Marín

Kripasindhu Sarkar

Alexander Schäfer

André Luiz Brandão

Ahmet Firintepe

Vladislav Golyanik

Deep Multi-State Object Pose Estimation for Augmented Reality Assembly

Deep Multi-State Object Pose Estimation for Augmented Reality Assembly
Yongzhi Su, Jason Raphael Rambach, Nareg Minaskan Karabid, Paul Lesur, Alain Pagani, Didier Stricker
Proceedings of the 18th IEEE ISMAR. IEEE International Symposium on Mixed and Augmented Reality (ISMAR-2019) October 14-18 Beijing China IEEE 2019 .

Abstract:: Neural network machine learning approaches are widely used for object classification or detection problems with significant success. A similar problem with specific constraints and challenges is object state estimation, dealing with objects that consist of several removable or adjustable parts. A system that can detect the current state of such objects from camera images can be of great importance for Augmented Reality(AR) or robotic assembly and maintenance applications. In this work, we present a CNN that is able to detect and regress the pose of an object in multiple states. We then show how the output of this network can be used in an automatically generated AR scenario that provides step-by-step guidance to the user in assembling an object consisting of multiple components.

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