TY - JOUR

T1 - Autocorrelation Descriptor for Efficient Co-Alignment of 3D Shape Collections

AU - Averkiou, Melinos

AU - Kim, Vladimir G.

AU - Mitra, Niloy J.

N1 - Publisher Copyright:
© 2015 The Eurographics Association and John Wiley & Sons Ltd.

PY - 2016/2/1

Y1 - 2016/2/1

N2 - 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.

AB - 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.

KW - digital geometry processing

KW - modeling

UR - http://www.scopus.com/inward/record.url?scp=84959209021&partnerID=8YFLogxK

U2 - 10.1111/cgf.12723

DO - 10.1111/cgf.12723

M3 - Article

AN - SCOPUS:84959209021

SN - 0167-7055

VL - 35

SP - 261

EP - 271

JO - Computer Graphics Forum

JF - Computer Graphics Forum

IS - 1

ER -