Image-Based Reconstruction of Spatially Varying Materials
The measurement of accurate material properties is an important step towards photorealistic rendering. Many real-world objects are composed of a number of materials that often show subtle changes even within a single material. Thus, for photorealistic rendering both the general surface properties as well as the spatially varying effects of the object are needed.
We present an image-based measuring method that robustly detects the different materials of real objects and fits an average bidirectional reflectance distribution function (BRDF) to each of them. In order to model the local changes as well, we project the measured data for each surface point into a basis formed by the recovered BRDFs leading to a truly spatially varying BRDF representation.
A high quality model of a real object can be generated with relatively few input data. The generated model allows for rendering under arbitrary viewing and lighting conditions and realistically reproduces the appearance of the original object.
KeywordsSurface Point Bidirectional Reflectance Distribution Function Global Illumination Radiance Sample Point Light Source
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- 2.P. Debevec, T. Hawkins, C. Tchou, H.-P. Duiker, W. Sarokin, and M. Sagar. Acquiring the Reflectance Field of a Human Face. In Proc. SIGGRAPH, pages 145–156, July 2000. ISBN 1-58113-208-5.Google Scholar
- 3.P. Debevec and J. Malik. Recovering High Dynamic Range Radiance Maps from Photographs. In Proc. SIGGRAPH, pages 369–378, August 1997.Google Scholar
- 4.P. Debevec, C. Taylor, and J. Malik. Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proc. SIGGRAPH, pages 11–20, August 1996.Google Scholar
- 5.M. Garland and P. Heckbert. Surface Simplification Using Quadric Error Metrics. In Proc. SIGGRAPH, pages 209–216, August 1997.Google Scholar
- 6.A. Gersho and R. Gray. Vector Quantization and Signal Compression. Kluwer Acad. Publishers, 1992.Google Scholar
- 7.S. Gortler, R. Grzeszczuk, R. Szelinski, and M. Cohen. The Lumigraph. In Proc. SIGGRAPH, pages 43–54, August 1996.Google Scholar
- 8.J. Kautz and H.-P. Seidel. Towards Interactive Bump Mapping with Anisotropic Shift-Variant BRDFs. In Eurographics/SIGGRAPH Hardware Workshop, pages 51–58, August 2000.Google Scholar
- 9.L. Kobbelt. Discrete fairing. In Proc. of the 7th IMA Corif. on the Mathematics of Surfaces, pages 101–131, 1996.Google Scholar
- 10.J. Koenderink, A. van Doom, and M. Stavridi. Bidirectional Reflection Distribution Function expressed in terms of surface scattering modes. In Proc. 4th Europ. Corif. on Computer Vision, pages 28–39, 1996.Google Scholar
- 11.E. Lafortune, S. Foo, K. Torrance, and D. Greenberg. Non-Linear Approximation of Reflectance Functions. In Proc. SIGGRAPH, pages 117–126, August 1997.Google Scholar
- 12.H. Lensch, W. Heidrich, and H.-P. Seidel. Automated Texture Registration and Stitching for Real World Models. In Pacific Graphics’ 00, pages 317–326, October 2000.Google Scholar
- 13.M. Levoy and P. Hanrahan. Light Field Rendering. In Proc. SIGGRAPH, pages 31–42, August 1996.Google Scholar
- 16.J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, volume I, 1967.Google Scholar
- 17.S. Marschner, S. Westin, E. Lafortune, K. Torrance, and D. Greenberg. Image-based BRDF Measurement Including Human Skin. In 10th Eurographics Workshop on Rendering, pages 131–144, June 1999.Google Scholar
- 18.G. Miller, S. Rubin, and D. Ponceleon. Lazy decompression of surface light fields for precomputed global illumination. In 9th Eurographics Workshop on Rendering, pages 281–292, June 1998.Google Scholar
- 19.S. Nayar, K. Ikeuchi, and T. Kanade. Recovering Shape in the Presence of Interreflections. In IEEE Int. Conf. on Robotics and Automation, pages 1814–1819, 1991.Google Scholar
- 20.W. Press, S. Teukolsky, W. Vetterling, and B. Flannery. Numerical Recipes in C; The Art of Scientific Computing (2nd ed.). Cambridge University Press, 1992. ISBN 0-521-43108-5.Google Scholar
- 21.H. Rushmeier, G. Taubin, and A. Guéziec. Applying Shape from Lighting Variation to Bump Map Capture. In 8th Eurographics Workshop on Rendering Workshop, pages 35–44, June 1997.Google Scholar
- 22.Y. Sato, M. Wheeler, and K. Ikeuchi. Object Shape and Reflectance Modeling from Observation. In Proc. SIGGRAPH, pages 379–388, August 1997.Google Scholar
- 23.H. Schirmacher, W. Heidrich, M. Rubick, D. Schiron, and H.-P. Seidel. Image-Based BRDF Reconstruction. In Proc. of the 4th VMV Conference, pages 285–292, November 1999.Google Scholar
- 24.K. Torrance and E. Sparrow. Theory for off-specular reflection from roughened surfaces. Journal of Optical Society of America, 57(9), 1967.Google Scholar
- 25.G. Ward Larson. Measuring and Modeling Anisotropic Reflection. In Proc. SIGGRAPH, pages 265–272, July 1992.Google Scholar
- 26.S. Westin, J. Arvo, and K. Torrance. Predicting Reflectance Functions From Complex Surfaces. In Proc. SIGGRAPH, pages 255–264, July 1992.Google Scholar
- 27.D. Wood, D. Azuma, K. Aldinger, B. Curless, T. Duchamp, D. Salesin, and W. Stuetzle. Surface Light Fields for 3D Photography. In Proc. SIGGRAPH, pages 287–296, July 2000.Google Scholar
- 28.Y. Yu, P. Debevec, J. Malik, and T. Hawkins. Inverse Global Illumination: Recovering Reflectance Models of Real Scenes From Photographs. In Proc. SIGGRAPH, pages 215–224, August 1999.Google Scholar
- 29.Z. Zhang. Flexible Camera Calibration By Viewing a Plane From Unknown Orientations. In Int. Conf. on Computer Vision, pages 666–673, September 1999.Google Scholar