Spatial Reflectance Recovery under Complex Illumination from Sparse Images

A major challenge in inverse reflectometry is the acquisition of spatially varying materials. In this paper, we introduce a method to recover spatial reflectance from a sparse set of images under general illumination. Specifically, we first remove the high-frequency varying diffuse reflection term by using a low-order spherical harmonic approximation. This allows us to directly estimate the specular properties with a cluster fitting process, which simplifies the fitting processes and addresses the problem of data inadequacy for sparse images. As a result, we can reconstruct a truly spatially varying BRDF model of the surface from less than 10 images. Experimental results will be presented in order to demonstrate the effectiveness of the proposed algorithm.

[1]  Pat Hanrahan,et al.  A signal-processing framework for inverse rendering , 2001, SIGGRAPH.

[2]  K. Torrance,et al.  Image-Based BRDF Measurement , 1999 .

[3]  Stephen H. Westin,et al.  Image-based bidirectional reflectance distribution function measurement. , 2000, Applied optics.

[4]  André Gagalowicz,et al.  Image-based rendering of diffuse, specular and glossy surfaces from a single image , 2001, SIGGRAPH.

[5]  Paul E. Debevec,et al.  Acquiring the reflectance field of a human face , 2000, SIGGRAPH.

[6]  Stephen H. Westin,et al.  Predicting reflectance functions from complex surfaces , 1992, SIGGRAPH.

[7]  Ronen Basri,et al.  Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Katsushi Ikeuchi,et al.  Object shape and reflectance modeling from observation , 1997, SIGGRAPH.

[9]  Hans-Peter Seidel,et al.  Image-Based Reconstruction of Spatially Varying Materials , 2001 .

[10]  Ravi Ramamoorthi,et al.  Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[12]  Donald P. Greenberg,et al.  Non-linear approximation of reflectance functions , 1997, SIGGRAPH.

[13]  Paul Debevec,et al.  Inverse global illumination: Recovering re?ectance models of real scenes from photographs , 1998 .