Simulated annealing & boltzmann Machines: a stochastic approach to combinatorialoptimization & neural computing
A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraining Object Features Using a Polarization Reflectance Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring and modeling anisotropic reflection
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
Polarization Phase-Based Method For Material Classification In Computer Vision
International Journal of Computer Vision
Improving Depth Image Acquisition Using Polarized Light
International Journal of Computer Vision
Illumination for computer generated pictures
Communications of the ACM
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Facts, Conjectures, and Improvements for Simulated Annealing
Facts, Conjectures, and Improvements for Simulated Annealing
Polarization-based Inverse Rendering from a Single View
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Incorporating the Torrance and Sparrow Model of Reflectance in Uncalibrated Photometric Stereo
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Transparent Surface Modeling from a Pair of Polarization Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the surface radiance function from single images
Graphical Models - Special issue: Vision and computer graphics
Testing new variants of the Beckmann-Kirchhoff model against radiance data
Computer Vision and Image Understanding
Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Estimation Using Polarization and Shading from Two Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polarization vision: a new sensory approach to image understanding
Image and Vision Computing
Recovery of surface orientation from diffuse polarization
IEEE Transactions on Image Processing
Estimating Facial Reflectance Properties Using Shape-from-Shading
International Journal of Computer Vision
Circularly polarized spherical illumination reflectometry
ACM SIGGRAPH Asia 2010 papers
Surface material segmentation using polarisation
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Natural material segmentation and classification using polarisation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Reflection component separation using statistical analysis and polarisation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Computational plenoptic imaging
ACM SIGGRAPH 2012 Courses
Estimating surface normals from spherical stokes reflectance fields
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Robust estimation of shape and polarisation using blind source separation
Pattern Recognition Letters
Hi-index | 0.00 |
This paper presents a novel technique for reflectance function (BRDF) estimation, which uses polarisation information and photometric stereo. The first stage of the technique is standard and involves the acquisition of polarisation information (angle and degree of polarisation) using a linear polariser and a digital camera. This yields a field of ambiguous surface normal estimates for an arbitrarily shaped object. A photometric stereo algorithm is then used with three different light source directions to disambiguate the surface normals. Next, the proposed algorithm constructs a 3D histogram of the surface normals and pixel brightnesses. A surface, representing the BRDF, is then fitted to the histogram data using simulated annealing optimisation. The result is a set of Cartesian triples that relate the surface normals to the observed pixel brightnesses. Unlike most previous techniques for BRDF estimation, the technique is image-based and does not require sophisticated equipment or intrusive light sources. Although the technique is restricted to smooth and slightly rough dielectric objects, no prior knowledge about the surface geometry is assumed.