Shape and Reflectance from Natural Illumination
We introduce a method to jointly estimate the BRDF and geometry of an object from a single image under known, but uncontrolled, natural illumination. We show that this previously unexplored problem becomes tractable when one exploits the orientation clues embedded in the lighting environment. Intuitively, unique regions in the lighting environment act analogously to the point light sources of traditional photometric stereo; they strongly constrain the orientation of the surface patches that reflect them. The reflectance, which acts as a bandpass filter on the lighting environment, determines the necessary scale of such regions. Accurate reflectance estimation, however, relies on accurate surface orientation information. Thus, these two factors must be estimated jointly. To do so, we derive a probabilistic formulation and introduce priors to address situations where the reflectance and lighting environment do not sufficiently constrain the geometry of the object. Through extensive experimentation we show what this space looks like, and offer insights into what problems become solvable in various categories of real-world natural illumination environments.
KeywordsLighting Environment Surface Patch Surface Orientation Angular Error Photometric Stereo
Unable to display preview. Download preview PDF.
- 1.Alldrin, N.G., Kriegman, D.J.: Toward Reconstructing Surfaces With Arbitrary Isotropic Reflectance: A Stratified Photometric Stereo Approach. In: IEEE Int’l Conf. on Computer Vision, pp. 1–8 (2007)Google Scholar
- 2.Alldrin, N.G., Zickler, T., Kriegman, D.J.: Photometric stereo with non-parametric and spatially-varying reflectance. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
- 5.Debevec, P.: Light Probe Image Gallery (2012), http://www.pauldebevec.com/Probes/
- 9.Huang, R., Smith, W.: Shape-from-Shading Under Complex Natural Illumination. In: IEEE Int’l Conf. on Image Processing, pp. 13–16 (2011)Google Scholar
- 10.Ikeuchi, K., Horn, B.K.P.: Numerical Shape from Shading and Occluding Boundaries. Artificial Intelligence (1981)Google Scholar
- 11.Johnson, M.K., Adelson, E.H.: Shape Estimation in Natural Illumination. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2011)Google Scholar
- 13.Lombardi, S., Nishino, K.: Single Image Multimaterial Estimation. In: IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 238–245 (2011)Google Scholar
- 15.Nishino, K.: Directional Statistics BRDF Model. In: IEEE Int’l Conf. on Computer Vision, pp. 476–483 (2009)Google Scholar
- 17.Rusinkiewicz, S.: A New Change of Variables for Efficient BRDF Representation. In: Eurographics Workshop on Rendering, pp. 11–22 (1998)Google Scholar
- 18.Woodham, R.J.: Photometric Method for Determining Surface Orientation from Multiple Images, vol. 19. MIT Press (1989)Google Scholar