Specularities Reduce Ambiguity of Uncalibrated Photometric Stereo
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Shape-from-Shading for Highlighted Surfaces
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Highlight Removal Using Shape-from-Shading
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Recovering 3-D shape and reflectance from a small number of photographs
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Realistic materials in computer graphics
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Bayesian object extraction from uncalibrated image pairs
Image Communication
Estimation of multiple directional illuminants from a single image
Image and Vision Computing
Reflection component separation using statistical analysis and polarisation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Inverse global illumination rendering for dense estimation of surface reflectance properties
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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To account for the variability of object appearance due to differences in illumination, attention has recently been focused on representing the set of images for all possible lighting conditions. Approaches that address this problem have primarily focused on lighting differences for diffuse reflection using the Lambertian model; however, specular reflections can additionally present considerable disparity in appearance.We present a method for representing illumination appearance for both diffuse and specular reflections for objects of uniform surface roughness using four photometric images. This approach uses separation of reflection components, extracts surface reflectances and roughness, and produces arbitrary lighting images without explicit computation of surface shape. Experimental results demonstrate the validity of the proposed method for constructing diffuse and specular appearances.