Clinical crowns shape reconstruction - An image-based approach

Precise knowledge of the 3D shape of clinical crowns is crucial for the treatment of malocclusion problems as well as several endodontic procedures. While Computed Tomography (CT) would present such information, it is believed that there is no threshold radiation dose below which it is considered safe. In this paper, we propose an image-based approach which allows for the construction of plausible human jaw models in vivo, without ionizing radiation, using fewer sample points in order to reduce the cost and intrusiveness of acquiring models of patients teeth/jaws over time. We assume that human teeth reflectance obeys Wolff-Oren-Nayar model where we experimentally prove that teeth surface obeys the microfacet theory. The inherent relation between the photometric information and the underlying 3D shape is formulated as a statistical model where the coupled effect of illumination and reflectance is modeled using the Helmhotlz Hemispherical Harmonics (HSH)-based irradiance harmonics whereas the Principle Component Regression (PCR) approach is deployed to carry out the estimation of dense 3D shapes. Vis-`a-vis dental applications, the results demonstrate a significant increase in accuracy in favor of the proposed approach where our system is evaluated on a database of 16 jaws.

[1]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  Aly A. Farag,et al.  Towards Efficient and Compact Phenomenological Representation of Arbitrary Bidirectional Surface Reflectance , 2011, BMVC.

[3]  Timothy F. Cootes,et al.  Statistical models of appearance for computer vision , 1999 .

[4]  J. Nelson,et al.  Characterization of dentin and enamel by use of optical coherence tomography. , 1999, Applied optics.

[5]  A. Glenny,et al.  European guidelines on radiation protection in dental radiology; the safe use of radiographs in dental practice. Biomed2 EU contract B4-3040/2001/326435/MAR/C4 , 2002 .

[6]  Jitendra Malik,et al.  Color Constancy, Intrinsic Images, and Shape Estimation , 2012, ECCV.

[7]  Jon Sporring,et al.  Bayes Reconstruction of Missing Teeth , 2008, Journal of Mathematical Imaging and Vision.

[8]  T. Vetter,et al.  A statistical method for robust 3D surface reconstruction from sparse data , 2004 .

[9]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Aly A. Farag,et al.  Novel Image-Based 3D Reconstruction of the Human Jaw using Shape from Shading and Feature Descriptors , 2011, BMVC.

[11]  Aly A. Farag,et al.  Model-Based Human Teeth Shape Recovery from a Single Optical Image with Unknown Illumination , 2012, MCV.

[12]  Yohan Payan,et al.  3D statistical models for tooth surface reconstruction , 2007, Comput. Biol. Medicine.

[13]  Aly A. Farag,et al.  Shape from shading for hybrid surfaces as applied to tooth reconstruction , 2010, 2010 IEEE International Conference on Image Processing.

[14]  Shree K. Nayar,et al.  Improved Diffuse Reflection Models for Computer Vision , 1998, International Journal of Computer Vision.

[15]  Jia Li,et al.  A novel 3D morphing approach for tooth occlusal surface reconstruction , 2011, Comput. Aided Des..

[16]  Olivier D. Faugeras,et al.  Unifying Approaches and Removing Unrealistic Assumptions in Shape from Shading: Mathematics Can Help , 2004, ECCV.

[17]  William A. P. Smith,et al.  3D morphable face models revisited , 2009, CVPR.

[18]  Aly A. Farag,et al.  Shape from Shading Under Various Imaging Conditions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.