Efficient panoramic sampling of real-world environments for image-based stereoscopic telepresence

Abstract

A key problem in telepresence systems is how to effectively emulate the subjective experience of being there delivered by our visual system. A step toward visual realism can be achieved by using high-quality panoramic snapshots instead of computer-based models of the scene. Furthermore, a better immersive illusion can be created by enabling the free viewpoint stereoscopic navigation of the scene, i.e. using omnistereoscopic imaging. However, commonly found implementation constraints of telepresence systems such as acquisition time, rendering complexity, and storage capacity, make the idea of using stereoscopic panoramas challenging. Having these constraints in mind, we developed a technique for the efficient acquisition and rendering of omnistereoscopic images based on sampling the scene with clusters of three panoramic images arranged in a controlled geometric pattern. Our technique can be implemented with any off-the-shelf panoramic cameras. Furthermore, it does not require neither the acquisition of additional depth information of the scene nor the estimation of camera parameters. The low the computational complexity and reduced data overhead of our rendering process make it attractive for the large scale stereoscopic sampling in a variety of scenarios.