Global Non-rigid Alignment of Surface Sequences

  • Authors:

  • Chris Budd;Peng Huang;Martin Klaudiny;Adrian Hilton

  • Affiliations:

  • Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK GU2 7XH;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK GU2 7XH;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK GU2 7XH;Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK GU2 7XH

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2013

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Abstract

This paper presents a general approach based on the shape similarity tree for non-sequential alignment across databases of multiple unstructured mesh sequences from non-rigid surface capture. The optimal shape similarity tree for non-rigid alignment is defined as the minimum spanning tree in shape similarity space. Non-sequential alignment based on the shape similarity tree minimises the total non-rigid deformation required to register all frames in a database into a consistent mesh structure with surfaces in correspondence. This allows alignment across multiple sequences of different motions, reduces drift in sequential alignment and is robust to rapid non-rigid motion. Evaluation is performed on three benchmark databases of 3D mesh sequences with a variety of complex human and cloth motion. Comparison with sequential alignment demonstrates reduced errors due to drift and improved robustness to large non-rigid deformation, together with global alignment across multiple sequences which is not possible with previous sequential approaches.