System identification
A robust-coded pattern projection for dynamic 3D scene measurement
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
3D shape recovery of complex objects from multiple silhouette images
Pattern Recognition Letters
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Robust refinement methods for camera calibration and 3D reconstruction from multiple images
Pattern Recognition Letters
Geometric calibration of a camera-projector 3D imaging system
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
A sampling theorem for a 2d surface
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
A Stratified Approach for Camera Calibration Using Spheres
IEEE Transactions on Image Processing
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The problem of 3D reconstruction from 2D captured images is solved using a set of cocentric circular light patterns. Once the number of light sources and cameras, their location and the orientations, and the sampling density (the number of circular patterns) are determined, we propose a novel approach to representation of the reconstruction problem as system identification. Akin to system identification using the relationship between input and output, to develop an efficient 3D functional camera system, we identify the reconstruction system by choosing / defining input and output signals appropriately. One algorithm states that an input and an output are defined as projected circular patterns and 2D captured image (overlaid with deformed circular patterns) respectively. Another one is that a 3D target and the captured 2D image are defined as the input and the output respectively, leading to a problem of input estimation by demodulating an output (received) signal. The former approach identifies the system from the ratio of output to input, and is akin to a modulation-demodulation theory, the latter identifies the reconstruction system by estimating the input signal. This paper proposes the approach to identification of reconstruction system, and also substantiates the algorithm by showing results using inexpensive and simple experimental setup.