## Abstract

Co-aligning a collection of shapes to a consistent pose is a common problem in shape analysis with applications in shape matching, retrieval and visualization. We observe that resolving among some orientations is easier than others, for example, a common mistake for bicycles is to align front-to-back, while even the simplest algorithm would not erroneously pick orthogonal alignment. The key idea of our work is to analyse rotational autocorrelations of shapes to facilitate shape co-alignment. In particular, we use such an autocorrelation measure of individual shapes to decide which shape pairs might have well-matching orientations; and, if so, which configurations are likely to produce better alignments. This significantly prunes the number of alignments to be examined, and leads to an efficient, scalable algorithm that performs comparably to state-of-the-art techniques on benchmark data sets, but requires significantly fewer computations, resulting in 2-16× speed improvement in our tests. Co-aligning a collection of shapes to a consistent pose is a common problem in shape analysis with applications in shape matching, retrieval and visualization. We observe that resolving among some orientations is easier than others, for example, a common mistake for bicycles is to align front-to-back, while even the simplest algorithm would not erroneously pick orthogonal alignment. The key idea of our work is to analyse rotational autocorrelations of shapes to facilitate shape co-alignment. In particular, we use such an autocorrelation measure of individual shapes to decide which shape pairs might have well-matching orientations; and, if so, which configurations are likely to produce better alignments. This significantly prunes the number of alignments to be examined, and leads to an efficient, scalable algorithm that performs comparably to state-of-the-art techniques on benchmark data sets, but requires significantly fewer computations, resulting in 2-16x speed improvement in our tests.

Original language | English (US) |
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Pages (from-to) | 261-271 |

Number of pages | 11 |

Journal | Computer Graphics Forum |

Volume | 35 |

Issue number | 1 |

DOIs | |

State | Published - Feb 1 2016 |

## Keywords

- digital geometry processing
- modeling

## ASJC Scopus subject areas

- Computer Graphics and Computer-Aided Design