A master-slaves volumetric framework for 3D reconstruction from images

A system reconstructing arbitrary shapes from images in real time and with enough accuracy would be paramount for a huge number of applications. The difficulty lies in the trade off between accuracy and computation time. Furthermore, given the image resolution and our real time needs, only a small number of cameras can be connected to a standard computer. The system needs a cluster and a strategy to share information. We introduce a framework for real time voxel based reconstruction from images on a cluster. From our point of view, the volumetric framework has five major advantages: an equivalent tree representation, an adaptable voxel description, an embedded multi-resolution capability, an easy fusion of shared information and an easy exploitation of inter-frame redundancy; and three minor disadvantage, its lack of precision with respect to method working at point level, its lack of global constraints on the reconstruction and the need of strongly calibrated cameras. Our goal is to illustrate the advantages and disadvantages of the framework in a practical example: the computation of the distributed volumetric inferred visual hull. The advantages and disadvantages are first discussed in general terms and then illustrated in the case of our concrete example.