Augmented Reality localisation using 6 DoF phantom head Pose Estimation-based generalisable Deep Learning model

Localisation and the estimation of the six degrees of freedom (6 DoF) object's position and orientation are essential tasks in numerous applications, such as augmented and virtual reality (AR and VR). It enables machines to gain a 3D understanding of their surroundings, empowering them to execute tasks like identifying objects, tracking their movement, interacting with them, and manipulating them. There is a growing trend in developing generalisable '6DoF' object pose estimators, which possess the ability to estimate the position and orientation of an unfamiliar object without the need for specific training or finetuning on that particular object during testing. By comparison, the previous 6 DoF object pose estimators focused primarily on specific categories of objects. This paper addresses the challenge of 6 DoF phantom head pose estimation from a single RGB image for augmented reality localisation. Using a local Magnetic Resonance Imaging (MRI) dataset, we reconstruct and print a 3D phantom head. This later is the target object for our AR localisation. Based on the comparison study including the accuracy and inference runtime scores, the Gen6D model achieves promising performance. Implementing 6 DoF pose estimation in deep learning stands to enhance and prove valuable in facilitating more effective interactions within the realm of augmented reality.