Three dimensional multi perspective imaging with randomly distributed sensors

In this paper, we review a three dimensional (3D) passive imaging system that exploits the visual information captured from the scene from multiple perspectives to reconstruct the scene voxel by voxel in 3D space. The primary contribution of this work is to provide a computational reconstruction scheme based on randomly distributed sensor locations in space. In virtually all of multi perspective techniques (e.g. integral imaging, synthetic aperture integral imaging, etc), there is an implicit assumption that the sensors lie on a simple, regular pickup grid. Here, we relax this assumption and suggest a computational reconstruction framework that unifies the available methods as its special cases. The importance of this work is that it enables three dimensional imaging technology to be implemented in a multitude of novel application domains such as 3D aerial imaging, collaborative imaging, long range 3D imaging and etc, where sustaining a regular pickup grid is not possible and/or the parallax requirements call for a irregular or sparse synthetic aperture mode. Although the sensors can be distributed in any random arrangment, we assume that the pickup position is measured at the time of caputre of each elemental image. We demonstrate the feasibility of the methods proposed here by experimental results.