The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2008 vol.30)
pp: 548-554
ABSTRACT
This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialise a multi-view photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: Firstly we describe a robust technique to estimate light directions and intensities and secondly, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and hence allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how even in the case of highly textured objects, this technique can greatly improve on correspondence-based multi-view stereo results.
INDEX TERMS
Shading, Stereo
CITATION
Carlos Hern?ndez Esteban, George Vogiatzis, Roberto Cipolla, "Multiview Photometric Stereo", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.30, no. 3, pp. 548-554, March 2008, doi:10.1109/TPAMI.2007.70820
REFERENCES
[1] M. Levoy, “Why Is 3D Scanning Hard?” Proc. 3D Processing, Visualization, Transmission, invited address, 2002.
[2] S. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski, “A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 519-528, 2006.
[3] R. Woodham, “Photometric Method for Determining Surface Orientation from Multiple Images,” Optical Eng., vol. 19, no. 1, pp. 139-144, 1980.
[4] J. Lim, J. Ho, M. Yang, and D. Kriegman, “Passive Photometric Stereo from Motion,” Proc. IEEE Int'l Conf. Computer Vision, vol. 2, pp. 1635-1642, Oct. 2005.
[5] J. Paterson, D. Claus, and A. Fitzgibbon, “BRDF and Geometry Capture from Extended Inhomogeneous Samples Using Flash Photography,” Proc. Eurographics '05, vol. 24, no. 3, pp. 383-391, 2005.
[6] F. Bernardini, H. Rushmeier, I. Martin, J. Mittleman, and G. Taubin, “Building a Digital Model of Michelangelo's Florentine Pieta,” IEEE Computer Graphics and Applications, vol. 22, no. 1, pp. 59-67, Jan./Feb. 2002.
[7] D. Nehab, S. Rusinkiewicz, J. Davis, and R. Ramamoorthi, “Efficiently Combining Positions and Normals for Precise 3D Geometry,” Proc. ACM SIGGRAPH, pp. 536-543, 2005.
[8] G. Vogiatzis, P. Favaro, and R. Cipolla, “Using Frontier Points to Recover Shape, Reflectance and Illumination,” Proc. IEEE Int'l Conf. Computer Vision, pp. 228-235, 2005.
[9] H. Jin, D. Cremers, A. Yezzi, and S. Soatto, “Shedding Light in Stereoscopic Segmentation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 36-42, 2004.
[10] O. Dbrohlav and M. Chandler, “Can Two Specular Pixels Calibrate Photometric Stereo?” Proc. IEEE Int'l Conf' Computer Vision, pp. 1850-1857, 2005.
[11] C. Hernández, F. Schmitt, and R. Cipolla, “Silhouette Coherence for Camera Calibration under Circular Motion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 343-349, Feb. 2007.
[12] A. Laurentini, “The Visual Hull Concept for Silhouette-Based Image Understanding,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 150-162, Feb. 1994.
[13] R. Cipolla and P. Giblin, Visual Motion of Curves and Surfaces. Cambridge Univ. Press, 1999.
[14] M. Fischler and R. Bolles, “Random Sample Consensus: A Paradigm for Model-Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[15] G. Vogiatzis, C. Hernández, and R. Cipolla, “Reconstruction in the Round Using Photometric Normals and Silhouettes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1847-1854, 2006.
[16] C. Hernández and F. Schmitt, “Silhouette and Stereo Fusion for 3D Object Modeling,” Computer Vision and Image Understanding, vol. 96, no. 3, pp. 367-392, Dec. 2004.
6 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool