Re-thinking non-rigid structure from motion

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Re-thinking non-rigid structure from motion. / Rabaud, Vincent; Belongie, Serge.

I: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Rabaud, V & Belongie, S 2008, 'Re-thinking non-rigid structure from motion', 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. https://doi.org/10.1109/CVPR.2008.4587679

APA

Rabaud, V., & Belongie, S. (2008). Re-thinking non-rigid structure from motion. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. https://doi.org/10.1109/CVPR.2008.4587679

Vancouver

Rabaud V, Belongie S. Re-thinking non-rigid structure from motion. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. 2008. https://doi.org/10.1109/CVPR.2008.4587679

Author

Rabaud, Vincent ; Belongie, Serge. / Re-thinking non-rigid structure from motion. I: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. 2008.

Bibtex

@inproceedings{6d913f9b23c6453f96e027fd99127729,
title = "Re-thinking non-rigid structure from motion",
abstract = "We present a novel approach to non-rigid structure from motion (NRSFM) from an orthographic video sequence, based on a new interpretation of the problem. Existing approaches assume the object shape space is well-modeled by a linear subspace. Our approach only assumes that small neighborhoods of shapes are well-modeled with a linear subspace. This constrains the shapes to belong to a manifold of dimensionality equal to the number of degrees of freedom of the object. After showing that the problem is still overconstrained, we present a solution composed of a novel initialization algorithm, followed by a robust extension of the Locally Smooth Manifold Learning algorithm tailored to the NRSFM problem. We finally present some test cases where the linear basis method fails (and is actually not meant to work) while the proposed approach is successful.",
author = "Vincent Rabaud and Serge Belongie",
year = "2008",
doi = "10.1109/CVPR.2008.4587679",
language = "English",
journal = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR",
note = "26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR ; Conference date: 23-06-2008 Through 28-06-2008",

}

RIS

TY - GEN

T1 - Re-thinking non-rigid structure from motion

AU - Rabaud, Vincent

AU - Belongie, Serge

PY - 2008

Y1 - 2008

N2 - We present a novel approach to non-rigid structure from motion (NRSFM) from an orthographic video sequence, based on a new interpretation of the problem. Existing approaches assume the object shape space is well-modeled by a linear subspace. Our approach only assumes that small neighborhoods of shapes are well-modeled with a linear subspace. This constrains the shapes to belong to a manifold of dimensionality equal to the number of degrees of freedom of the object. After showing that the problem is still overconstrained, we present a solution composed of a novel initialization algorithm, followed by a robust extension of the Locally Smooth Manifold Learning algorithm tailored to the NRSFM problem. We finally present some test cases where the linear basis method fails (and is actually not meant to work) while the proposed approach is successful.

AB - We present a novel approach to non-rigid structure from motion (NRSFM) from an orthographic video sequence, based on a new interpretation of the problem. Existing approaches assume the object shape space is well-modeled by a linear subspace. Our approach only assumes that small neighborhoods of shapes are well-modeled with a linear subspace. This constrains the shapes to belong to a manifold of dimensionality equal to the number of degrees of freedom of the object. After showing that the problem is still overconstrained, we present a solution composed of a novel initialization algorithm, followed by a robust extension of the Locally Smooth Manifold Learning algorithm tailored to the NRSFM problem. We finally present some test cases where the linear basis method fails (and is actually not meant to work) while the proposed approach is successful.

UR - http://www.scopus.com/inward/record.url?scp=51949102217&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2008.4587679

DO - 10.1109/CVPR.2008.4587679

M3 - Conference article

AN - SCOPUS:51949102217

JO - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

JF - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Y2 - 23 June 2008 through 28 June 2008

ER -

ID: 302050887