Bibliographic description:
Vorobiov, Vitalii. On 3D Pose Estimation for XR. Classic Approaches vs Neural Networks / Vitalii Vorobiov; Supervisor: Dr. Taras Hapko; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2021. – 32 p.: ill.
Abstract:
The primary purpose of this paper is to investigate different approaches for 3D object
pose estimation, which uses neural networks, and for model-based tracking - an
innovative solution that builds upon a combination of known matching and pose
estimation algorithms and to propose the one which will be more suitable for our
problem. Object tracking is one of the critical problems for many applications on
AR/MR devices that use object pose estimation to create an immersive experience by
combining the physical world with virtually generated objects. The main limitation
of our application is that it must work in real-time and be efficient enough to run on
devices with weak computing power (e.g., RealWear HMT-1).
