Robust regression and outlier detection
Robust regression and outlier detection
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Performance relighting and reflectance transformation with time-multiplexed illumination
ACM SIGGRAPH 2005 Papers
Reflectance Sharing: Predicting Appearance from a Sparse Set of Images of a Known Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust estimation of surface properties and interpolation of shadow/specularity components
Image and Vision Computing
Reflectance sharing: image-based rendering from a sparse set of images
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
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Polynomial Texture Mapping (PTM) is a technique employed in a variety of settings, from museums to in-the-field image capture to multi-illuminant microscopy. It consists of illuminating the surface in question with lights from a collection of light directions, each light in turn. To date, the most accurate interpolation employed in PTM consists of two stages: a matte regression stage followed by a further specularity/shadow interpolation. For the first stage, recovering an underlying matte model so as to acquire surface albedo, normals and chromaticity, PTM employs polynomial regression at each pixel, mapping light-direction to luminance. A more accurate model excludes outlier values deriving from specularities and shadows by employing a robust regression from 6-D polynomials to 1-D luminance. Robust methods are guaranteed to automatically find the best representation of the underlying matte content. Here, we retain the idea of using robust methods but instead investigate using a much simpler robust 1-D mode-finder, acting on luminance and on chromaticity components. We then go on to increase accuracy by carrying out 3-D to 1-D regression: this strikes a balance between the best method and the fastest method, with greatly diminished complexity and another large speedup. We show that little accuracy is lost using this much simpler method, and demonstrate the effectiveness of the new method on several image datasets.