IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Factorial Markov Random Fields
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A data-driven reflectance model
ACM SIGGRAPH 2003 Papers
Example-Based Photometric Stereo: Shape Reconstruction with General, Varying BRDFs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photometric Stereo with General, Unknown Lighting
International Journal of Computer Vision
Numerical methods for shape-from-shading: A new survey with benchmarks
Computer Vision and Image Understanding
Shape and Spatially-Varying BRDFs from Photometric Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape estimation in natural illumination
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Single image multimaterial estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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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.