TILT: Transform Invariant Low-Rank Textures

  • Authors:

  • Zhengdong Zhang;Arvind Ganesh;Xiao Liang;Yi Ma

  • Affiliations:

  • Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, USA;Institute for Advanced Study, Tsinghua University, Beijing, China;Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, USA and Visual Computing Group, Microsoft Research Asia, Beijing, China

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2012

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Abstract

In this paper, we propose a new tool to efficiently extract a class of "low-rank textures" in a 3D scene from user-specified windows in 2D images despite significant corruptions and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as many kinds of regular, symmetric patterns ubiquitous in urban environments and man-made objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant affine or projective deformation, our method can accurately recover both the intrinsic low-rank texture and the unknown transformation, and hence both the geometry and appearance of the associated planar region in 3D. Extensive experimental results demonstrate that this new technique works effectively for many regular and near-regular patterns or objects that are approximately low-rank, such as symmetrical patterns, building facades, printed text, and human faces.