Semantic Augmented Reality Environment with Material-Aware Physical Interactions
This addresses the need for high-level semantic understanding in AR to enhance user experience and application design, representing an incremental advancement over geometry-focused approaches.
The paper tackles the problem of enabling realistic physical interactions between virtual and real objects in Augmented Reality (AR) by developing a material-aware AR environment using deep material learning with a fully convolutional network (FCN), achieving natural interactions for interactive AR games.
In Augmented Reality (AR) environment, realistic interactions between the virtual and real objects play a crucial role in user experience. Much of recent advances in AR has been largely focused on developing geometry-aware environment, but little has been done in dealing with interactions at the semantic level. High-level scene understanding and semantic descriptions in AR would allow effective design of complex applications and enhanced user experience. In this paper, we present a novel approach and a prototype system that enables the deeper understanding of semantic properties of the real world environment, so that realistic physical interactions between the real and the virtual objects can be generated. A material-aware AR environment has been created based on the deep material learning using a fully convolutional network (FCN). The state-of-the-art dense Simultaneous Localisation and Mapping (SLAM) has been used for the semantic mapping. Together with efficient accelerated 3D ray casting, natural and realistic physical interactions are generated for interactive AR games. Our approach has significant impact on the future development of advanced AR systems and applications.