Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Dense variational reconstruction of non-rigid surfaces from monocular video
Author: Garg, R.
Roussos, A.
Agapito, L.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp.1272-1279
Publisher: IEEE
Issue Date: 2013
ISBN: 9780769549897
ISSN: 1063-6919
Conference Name: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013) (23 Jun 2013 - 28 Jun 2013 : Portland, Oregon)
Statement of
Ravi Garg, Anastasios Roussos, Lourdes Agapito
Abstract: This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate non-rigid structure from motion (nrsfm) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along with the camera motion matrices, given dense 2D correspondences. Unlike traditional factorization based approaches to nrsfm, which model the low-rank non-rigid shape using a fixed number of basis shapes and corresponding coefficients, we minimize the rank of the matrix of time-varying shapes directly via trace norm minimization. In conjunction with this low-rank constraint, we use an edge preserving total-variation regularization term to obtain spatially smooth shapes for every frame. Thanks to proximal splitting techniques the optimization problem can be decomposed into many point-wise sub-problems and simple linear systems which can be easily solved on GPU hardware. We show results on real sequences of different objects (face, torso, beating heart) where, despite challenges in tracking, illumination changes and occlusions, our method reconstructs highly deforming smooth surfaces densely and accurately directly from video, without the need for any prior models or shape templates.
Rights: © 2013 IEEE
DOI: 10.1109/CVPR.2013.168
Published version:
Appears in Collections:Aurora harvest 3
Computer Science publications

Files in This Item:
File Description SizeFormat 
  Restricted Access
Restricted Access1.6 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.