Abstract
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.
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Lee, D., Krim, H.: 3D Surface Reconstruction Using Structured Circular Light Patterns. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part I. LNCS, vol. 6474, pp. 279–289. Springer, Heidelberg (2010)
Hor, M.-K., Tang, C.-Y., Wu, Y.-L., Chan, K.-H., Tsai, J.-J.: Robust refinement methods for camera calibration and 3D reconstruction from multiple images. Pattern Recognition Letters 32(8), 1210–1221 (2011)
Li, T., Hu, F., Geng, Z.: Geometric Calibration of a Camera-Projector 3D Imaging System. In: Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry, VRCAI 2011, pp. 187–194 (2011)
Wong, K.-Y.K., Zhang, G., Chen, Z.: A Stratified Approach for Camera Calibration Using Spheres. IEEE Transactions on Image Processing 20(2), 305–316 (2011)
Zahn, C.T., Roskies, R.Z.: Fourier Descriptors for Plane Closed Curves. IEEE Transactions on Computers C-21(3), 269–281 (1972)
Faugeras, O., Luong, Q.-T.: The Geometry of Multiple Images. The MIT Press Cambridge, Massachusetts
Proakis, J.G.: Digital Communications, 4th edn. McGraw Hill
Bruno, F., Bianco, G., Muzzupappa, M., Barone, S., Razionale, A.V.: Experimentation of structured light and stereo vision for underwater 3D reconstruction. Journal of Photogrammetry and Remote Sensing 66, 508–518 (2011)
Salvi, J., Batlle, J., Mouaddib, E.: A robust-coded pattern projection for dynamic 3D scene measurement. Pattern Recognition Letters 19(11), 1055–1065 (1998)
Fang, Y.-H., Chou, H.-L., Chen, Z.: 3D shape recovery of complex objects from multiple silhouette images. Pattern Recognition Letters 24(9-10), 1279–1293 (2003)
Geng, J.: Structured-light 3D surface imaging: a tutorial. Advances in Optics and Photonics 3(2), 128–160 (2011)
Papoulis, A.: Signal Analysis. McGraw-Hill (1977)
Lee, D., Krim, H.: A Sampling Theorem for a 2D Surface. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds.) SSVM 2011. LNCS, vol. 6667, pp. 556–567. Springer, Heidelberg (2012)
Söderström, T., Stoica, P.: System Identification. Prentice Hall (1989)
Iwahori, Y., Sugie, H., Ishii, N.: Reconstructing shape from shading images under point light source illumination. In: 10th International Conference on Pattern Recognition (1990)
Zhang, R., Tsai, P.-S., Cryer, J.E., Shah, M.: Shape-from-shading: a survey. Pattern Analysis and Machine Intelligence. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 690–706
Lee, D., Krim, H.: Surface Reconstruction: Multiple-View-Projector System. In: International Conference on Image Processing (submitted, 2012)
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Lee, D., Krim, H. (2012). System Identification: 3D Measurement Using Structured Light System. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_1
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DOI: https://doi.org/10.1007/978-3-642-33140-4_1
Publisher Name: Springer, Berlin, Heidelberg
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