Article

International Journal of Computer Vision

, Volume 102, Issue 1, pp 256-270

Open Access This content is freely available online to anyone, anywhere at any time.

Global Non-rigid Alignment of Surface Sequences

  • Chris BuddAffiliated withCentre for Vision, Speech and Signal Processing, University of Surrey
  • , Peng HuangAffiliated withCentre for Vision, Speech and Signal Processing, University of Surrey
  • , Martin KlaudinyAffiliated withCentre for Vision, Speech and Signal Processing, University of Surrey
  • , Adrian HiltonAffiliated withCentre for Vision, Speech and Signal Processing, University of Surrey Email author 

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.

Keywords

Non-rigid surface alignment Surface tracking Non-sequential tracking 3D video 3D mesh sequences 4D modelling