Free-Form Mesh Tracking: a Patch-Based Approach

Cedric Cagniart 1 Edmond Boyer 2 Slobodan Ilic 1
2 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : In this paper, we consider the problem of tracking non-rigid surfaces and propose a generic data-driven mesh deformation framework. In contrast to methods using strong prior models, this framework assumes little on the observed surface and hence easily generalizes to most free-form surfaces while effectively handling large deformations. To this aim, the reference surface is divided into elementary surface cells or patches. This strategy ensures robustness by providing natural integration domains over the surface for noisy data, while enabling to express simple patch-level rigidity constraints. In addition, we associate to this scheme a robust numerical optimization that solves for physically plausible surface deformations given arbitrary constraints. In order to demonstrate the versatility of the proposed framework, we conducted experiments on open and closed surfaces, with possibly non-connected components, that undergo large deformations and fast motions. We also performed quantitative and qualitative evaluations in multi-cameras and monocular environments, and with different types of data including 2D correspondences and 3D point clouds.
Document type :
Conference papers
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download


https://hal.inria.fr/inria-00568909
Contributor : Edmond Boyer <>
Submitted on : Thursday, February 24, 2011 - 10:54:49 AM
Last modification on : Wednesday, April 11, 2018 - 1:57:48 AM
Long-term archiving on : Tuesday, November 6, 2012 - 2:51:17 PM

Files

cagniart2010CVPR.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Cedric Cagniart, Edmond Boyer, Slobodan Ilic. Free-Form Mesh Tracking: a Patch-Based Approach. CVPR 2010 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2010, San Francisco, United States. pp.1339-1346, ⟨10.1109/CVPR.2010.5539814⟩. ⟨inria-00568909⟩

Share

Metrics

Record views

661

Files downloads

715