Three dimensional imaging with randomly distributed sensors

As a promising three dimensional passive imaging modality, Integral Imaging (II) has been investigated widely within the research community. In virtually all of such investigations, there is an implicit assumption that the collection of elemental images lie on a simple geometric surface (e.g. flat, concave, etc), also known as pickup surface. In this paper, we present a generalized framework for 3D II with arbitrary pickup surface geometry and randomly distributed sensor configuration. In particular, we will study the case of Synthetic Aperture Integral Imaging (SAII) with random location of cameras in space, while all cameras have parallel optical axes but different distances from the 3D scene. We assume that the sensors are randomly distributed in 3D volume of pick up space. For 3D reconstruction, a finite number of sensors with known coordinates are randomly selected from within this volume. The mathematical framework for 3D scene reconstruction is developed based on an affine transform representation of imaging under geometrical optics regime. We demonstrate the feasibility of the methods proposed here by experimental results. To the best of our knowledge, this is the first report on 3D imaging using randomly distributed sensors.