A sampling criterion for a surface reconstruction using structured light patterns is derived in this article. The problem of signal recovery from its samples is addressed, and a salient spatial frequency component of an object is defined for a sampling rate determination. The authors introduce 3D object coordinate extraction from a 2D captured image by employing structured light systems (a set of concentric circular light patterns), without any prior information about an object of interest. To achieve an efficient 3D reconstruction (approximate reconstruction result), a minimal sampling rate, defined as the minimum number of circular patterns to be projected onto the 3D scene, is derived using specific geometric information (e.g., curvature) of an object. This article chiefly deals with theoretical algorithms for efficient 3D scene reconstruction, recognition and other applications of 3D imaging techniques, and the proposed algorithms can be applied to diverse applications in the field of computer vision, 3D graphics, etc.