We propose a robust photometric stereo method by using structural arrangement of light sources. In the arrangement, light sources are positioned on a planar grid and form a set of collinear combinations. The shadow pixels are detected by adaptive thresholding. The specular highlight and diffuse pixels are distinguished according to their intensity deviations of the collinear combinations, thanks to the special arrangement of light sources. The highlight detection problem is cast as a pattern classification problem and is solved using support vector machine classifiers. Considering the possible misclassification of highlight pixels, the ℓ1 regularization is further employed in normal map estimation. Experimental results on both synthetic and real-world scenes verify that the proposed method can robustly recover the surface normal maps in the case of heavy specular reflection and outperforms the state-of-the-art techniques.
In this paper, we propose a novel post-alignment method. The method is both simple and effective for stereo video postproduction.
A low-distortion algorithm for rectifying the epipolar lines is first introduced. Unlike traditional methods,
which map the epipoles to (1,0,0) <sup>T</sup> directly, our method conducts it in two steps: 1) mapping the epipoles to points at
infinity; 2) aligning the epipolar lines with x-axis. More specifically, by taking advantage of that commonly available
stereoscopic movies are nearly aligned, our method keeps one of the stereo images unchanged, and the rectification is
only applied to the other image. Besides epipolar non-parallel distortions, disparity distortion is also an important issue
to consider for the stereoscopic movie. We propose a new constraint for stereoscopic video alignment such that the
variations of disparities is also minimized. Experimental results have demonstrated that our method obtains better visual
effect than the state-of-the-art methods.