Surface Reconstruction from Intensity Image Using Illumination Model Based Morphable Modeling

• Published in: Computer Vision Systems Vol. 9163; pp. 117 - 127
• Main Authors: Yang, Zhi, Chandola, Varun
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: 3d surfaces; Depth reconstruction; Graphics programming; Human perception; Image processing; Morphable modeling; Pattern recognition; SFS; Surface deforming
• ISBN: 9783319209036; 3319209035
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-20904-3_11

We present a new method for reconstructing depth of a known object from a single still image using deformed underneath sign matrix of a similar object. Existing Shape from Shading(SFS) methods try to establish a relationship between intensity values of a still image and surface normal of corresponding depth, but most of them resort to error minimization based approaches. Given the fact that these reconstruction approaches are fundamentally ill-posed, they have limited successes for surfaces like a human face. Photometric Stereo (PS) or Structure from Motion (SfM) based methods extend SFS by adding additional information/constraints about the target. Our goal is identical to SFS, however, we tackle the problem by building a relationship between gradient of depth and intensity value at the corresponding location of image of the same object. This formula is simplified and approximated for handing different materials, lighting conditions and, the underneath sign matrix is also obtained by resizing/deforming Region of Interest(ROI) with respect to its counterpart of a similar object. The target object is then reconstructed from its still image. In addition to the process, delicate details of the surface is also rebuilt using a Gabor Wavelet Network(GWN) on different ROIs. Finally, for merging the patches together, a Self-Organizing Maps(SOM) based method is used to retrieve and smooth boundary parts of ROIs. Compared with state of art SFS based methods, the proposed method yields promising results on both widely used benchmark datasets and images in the wild.