Deep 3D Model Optimization for Immersive and Interactive Applications

Elena Camuffo, Federica Battisti, Francesco Pham, Simone Milani

Abstract: The growing diffusion of immersive and interactive applications is posing new challenges in the multimedia processing chain. When dealing with AR and VR applications, the most relevant aspects to consider are the (1) quality of the visualized 3D objects and (2) the fluidity in the visualization in case the user is moving in the environment. In this framework, we propose a deep learning based approach that estimates the optimal model parameters to be used in relation to the viewer’s movement and the model characteristics and quality. The performed tests show the effectiveness of the proposed approach.