Superimposing 3D Models on Real Scenes Based on The Reinforcement Learning using Visual Observations

This research presents a method for Augmented Reality (AR) object superimposition leveraging reinforcement learning techniques to significantly reduce manual input during the exploration of digital information on construction sites. A distinctive feature of this approach is the application of a reinforcement learning neural network, trained with pairs of real and virtual view images, for AR superimposition. This approach enables the precise adjustment of the virtual camera's position and orientation within a virtual scene, aiming to seamlessly integrate AR objects into real-world views. This research initially focuses on simpler scenarios involving 2 and 3 degrees of freedom for orientation and position adjustments. The purpose is to explore the feasibility of the application through those experiments, with the expectation that the results would be interpreted positively. These initial findings highlight the promise of the suggested method in improving AR applications, especially within the construction sector, by enabling more natural and precise merging of virtual and physical objects, without requiring user intervention.