Measuring and modeling anisotropic reflection
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
Object shape and reflectance modeling from observation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A Theory of Specular Surface Geometry
International Journal of Computer Vision
Inverse global illumination: recovering reflectance models of real scenes from photographs
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination for computer generated pictures
Communications of the ACM
Image-based rendering of diffuse, specular and glossy surfaces from a single image
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
A signal-processing framework for inverse rendering
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Estimation of Illuminant Direction and Intensity of Multiple Light Sources
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Image-Based Reconstruction of Spatially Varying Materials
Proceedings of the 12th Eurographics Workshop on Rendering Techniques
Estimation of Multiple Directional Light Sources for Synthesis of Mixed Reality Images
PG '02 Proceedings of the 10th Pacific Conference on Computer Graphics and Applications
Polarization-based Inverse Rendering from a Single View
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiple-cue Illumination Estimation in Textured Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A signal-processing framework for reflection
ACM Transactions on Graphics (TOG)
Specular Flow and the Recovery of Surface Structure
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and materials by example: a photometric stereo approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Specularity removal in images and videos: a PDE approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Accurately estimating reflectance parameters for color and gloss reproduction
Computer Vision and Image Understanding
Camera and light calibration from reflections on a sphere
Computer Vision and Image Understanding
International Journal of Computer Vision
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Recovering multiple point light sources from a sparse set of photographs in which objects of unknown texture can move is challenging. This is because both diffuse and specular reflections appear to slide across surfaces, which is a well known physical fact. What is seldom demonstrated, however, is that it can be taken advantage of to address the light source recovery problem. In this paper, we therefore show that, if approximate 3D models of the moving objects are available or can be computed from the images, we can solve the problem without any a priori constraints on the number of sources, on their color, or on the surface albedos. Our approach involves finding local maxima in individual images, checking them for consistency across images, retaining the apparently specular ones, and having them vote in a Hough-like scheme for potential light source directions. The precise directions of the sources and their relative power are then obtained by optimizing a standard lighting model. As a byproduct we also obtain an estimate of various material parameters such as the unlighted texture and specular properties. We show that the resulting algorithm can operate in presence of arbitrary textures and an unknown number of light sources of possibly different unknown colors. We also estimate its accuracy using ground-truth data.