Abstract: 

We present a photometric stereo method for non-diffuse materials that does not require an explicit reflectance model or reference object. By computing a data-dependent rotation of RGB color space, we show that the specular reflection effects can be separated from the much simpler, diffuse (approximately Lambertian) reflection effects for surfaces that can be modeled with dichromatic reflectance. Images in this transformed color space are used to obtain photometric reconstructions that are independent of the specular reflectance. In contrast to other methods for highlight removal based on dichromatic color separation (e.g., color histogram analysis and/or polarization), we do not explicitly recover the specular and diffuse components of an image. Instead, we simply find a transformation of color space that yields more direct access to shape information. The method is purely local and is able to handle surfaces with arbitrary texture.

RGB Color Image	S Channel	U Channel	V Channel

1. Qualitative Results:
   
• The above results show qualitative results of photometric stereo using the SUV image. Three color images of a specular object were acquired under three different lighting conditions.  The images were traformed to the SUV color space by a rotation of the RGB color space. The U and V channels of the images were used for photometric stereo. The first image shows one of the three acquired images. The second image shows the recovered surface and the third image shows the texture mapped surface.
  
2. Quantitative Results: 
• In the above results, the surfaces of five spheres with increasing specularity were reconstructed.  The results of other photometric stereo methods for specular surfaces were compared to our method.

• Satya P. Mallick, Todd E. Zickler, David J. Kriegman, and Peter N. Belhumeur, "Beyond Lambert: Reconstructing Specular Surfaces Using Color."  in Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2005.