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In this paper, a dual imaging technique is used for high-precision reconstruction of an observed 3D scene. In contrast to stereo vision, dual imaging systems use a camera and a projector instead of a camera pair. We propose a multiresolution approach based on the sum-to-one transform, coupled with compressive sensing principles, for efficient estimation of the light transport matrix (LTM). The LTM contains information on both optical systems and the 3D scene. In our setup, the camera sensor is intentionally chosen to be low resolution to prove the future use of inexpensive sensors in nonvisible regions of the light spectrum, as well as the potential design of simplified multiview and light field acquisition systems. We show that a highprecision estimation of the LTM from a reduced set of measurements is possible. Virtual measurements, instead of physical, are conducted to obtain the 3D reconstruction. We show that 3D scene reconstruction from the proposed virtual measurements corresponds with the actual physical acquisition. Moreover, this approach provides much more detail in the reconstruction. The computational complexity of the proposed methods is reduced to such a level that practical implementations are feasible.

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