Surface Normal Deconvolution: Photometric Stereo for Optically Thick Translucent Objects

Computer Vision – ECCV 2014 13th European Conference, Zurich, Switzerland, September 6-12, 2014,This paper presents a photometric stereo method that works for optically thick translucent objects exhibiting subsurface scattering. Our method is built upon the previous studies showing that subsurface scattering is approximated as convolution with a blurring kernel. We extend this observation and show that the original surface normal convolved with the scattering kernel corresponds to the blurred surface normal that can be obtained by a conventional photometric stereo technique. Based on this observation, we cast the photometric stereo problem for optically thick translucent objects as a deconvolution problem, and develop a method to recover accurate surface normals. Experimental results of both synthetic and real-world scenes show the effectiveness of the proposed method

Solving the Uncalibrated Photometric Stereo Problem using Total Variation

International audienceIn this paper we propose a new method to solve the problem of uncalibrated photometric stereo, making very weak assumptions on the properties of the scene to be reconstructed. Our goal is to solve the generalized bas-relief ambiguity (GBR) by performing a total variation regularization of both the estimated normal field and albedo. Unlike most of the previous attempts to solve this ambiguity, our approach does not rely on any prior information about the shape or the albedo, apart from its piecewise smoothness. We test our method on real images and obtain results comparable to the state-of-the-art algorithms

Destriping Satellite Images

Before satellite images obtained with multiple image sensors can be used in image analysis, corrections must be introduced for the differences in transfer functions on these sensors. Methods are here presented for obtaining the required information directly from the statistics of the sensor outputs. The assumption is made that the probability distribution of the scene radiance seen by each image sensor is the same. Successful destriping of LANDSAT images is demonstrated

Robust Luminance and Chromaticity for Matte Regression in Polynomial Texture Mapping

Abstract. 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 lightdirection 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.

Edge detection on polynomial texture maps

In this paper we propose a simple method to extract edges from Polynomial Texture Maps (PTM) or other kinds of Reflection Transformation Image (RTI) files. It is based on the idea of following 2D lines where the variation of corresponding 3D normals computed from the PTM coefficients is maximal. Normals are estimated using a photometric stereo approach, derivatives along image axes directions are computed in a multiscale framework providing normal discontinuity and orientation maps and lines are finally extracted using non-maxima suppression and hysteresis thresholds as in Canny's algorithm. In this way it is possible to discover automatically potential structure of interest (inscriptions, small reliefs) on Cultural Heritage artifacts of interest without the necessity of interactively recreating images using different light directions. Experimental results obtained on test data and new PTMs acquired in an archaeological site in the Holy Land with a simple low-end camera, show that the method provides potentially useful results