Self-training based augmented reality for robust 3D object registration and task assistance

Abstract

Typical augmented reality (AR) technologies superimpose virtual information onto the real environment for task guidance and assistance by tracking fiducial markers. However, these have narrow recognition ranges due to the markers, and it is difficult to track markers when occlusion occurs. Therefore, it is challenging to seamlessly register real objects in AR scenes captured by the camera without requiring fiducial markers. Deep learning–based approaches to 3D object registration and task assistance have recently indicated promising directions for superimposing 3D virtual objects onto corresponding real objects or their surroundings seamlessly. However, since supervised learning-based object registration requires many pose annotations on images for the locations and orientations of real objects, this process is a time-consuming and labor-intensive task. Thus, this paper proposes a new self-training based augmented reality method for seamless real object registration and task assistance without time-consuming pose annotations. As the industrial components or objects have their 3D geometric models, the proposed approach utilizes them as their synthetic data. The automatically generated synthetic dataset is used for training the deep learning model, which outputs a colored coordinate map of objects to be detected. 2D–3D key point matching between the 2D colored coordinate map and 3D colored coordinate model is then performed for 3D object registration in an AR environment. In particular, self-training is performed for fine-tuning the deep learning model by generating pseudo-labels with the highest probability for mask intersection over union (IoU) in the unlabeled real object dataset. We compared the proposed approach with previous studies to confirm its effectiveness and advantages. Also, we performed ablation studies by training a custom dataset based on various industrial 3D virtual models. Finally, we present AR-based task guidance and assistance, such as industrial robot manipulation.