Shape from Texture without Boundaries

We describe a shape from texture method that constructs a maximum a posteriori estimate of surface coefficients using only the deformation of individual texture elements. Our method does not need to use either the boundary of the observed surface or any assumption about the overall distribution of elements. The method assumes that texture elements are of a limited number of types of fixed shape. We show that, with this assumption and assuming generic view and texture, each texture element yields the surface gradient unique up to a two-fold ambiguity. Furthermore, texture elements that are not from one of the types can be identified and ignored. An EM-like procedure yields a surface reconstruction from the data. The method is defined for othographic views -- an extension to perspective views appears to be complex, but possible. Examples of reconstructions for synthetic images of surfaces are provided, and compared with ground truth. We also provide examples of reconstructions for images of real scenes. We show that our method for recovering local texture imaging transformations can be used to retexture objects in images of real scenes.

[1]  Jonas Gårding,et al.  Shape from Texture for Smooth Curved Surfaces , 1992, ECCV.

[2]  Stéphane Mallat,et al.  Shape from texture through deformations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Jitendra Malik,et al.  Computing Local Surface Orientation and Shape from Texture for Curved Surfaces , 1997, International Journal of Computer Vision.

[4]  David A. Forsyth,et al.  Shape from texture and integrability , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Jonas Gårding,et al.  Surface orientation and curvature from differential texture distortion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Lorenzo Torresani,et al.  Tracking and modeling non-rigid objects with rank constraints , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  Jitendra Malik,et al.  Surface orientation from texture: Isotropy or homogeneity (or both)? , 1997, Vision Research.

[9]  Andrew Blake,et al.  Shape from Texture: Estimation, Isotropy and Moments , 1990, Artif. Intell..

[10]  Andrew Zisserman,et al.  Geometric Grouping of Repeated Elements within Images , 1999, Shape, Contour and Grouping in Computer Vision.

[11]  Andrew P. Witkin,et al.  Recovering Surface Shape and Orientation from Texture , 1981, Artif. Intell..

[12]  Joseph L. Mundy,et al.  Repeated Structures: Image Correspondence Constraints and 3D Structure Recovery , 1993, Applications of Invariance in Computer Vision.

[13]  Jitendra Malik,et al.  Detecting, localizing and grouping repeated scene elements from an image , 1996, ECCV.