Stage Master/Master internship 2012

Estimation of local surface rigidity parameters of tracked 3D shapes

Advisors

Jean-Sebastien Franco, Morpheo team
Edmond Boyer, Morpheo Team
LJK & Inria
E-mail : jean-sebastien.franco@inria.fr
Tel.: 04 56 52 71 30
E-mail : edmond.boyer@inrialpes.fr
Tel.: 04 76 61 53 54

Context

This project is part of the long term research effort in the Morpheo team to identify intrinsic properties of objects in motion, as observed with multi-camera video streams. Assuming a template 3D model is available for the observed shape, our goal in this master 2 internship is to define a local model of surface rigidity and estimate its parameters given a set of multi-view videos where the object is observed moving.

Pre-requisites include a background and strong interest in computer vision and probabilistic modeling, skills in computer graphics or applied geometry are a plus.

Objectives

Different strategies exist to identify the rigid structure of a geometric model. A number of methods segment static models based on geometry alone [1], but this assumes a correlation between geometric features and the underlying motion partition, and as such does not segment according to the kinematic definition of rigidity.

A number of computer vision techniques successfully segment moving point clouds into rigid parts based on point motions alone [2], yet do not attempt to leverage or infer proximity information on the observed surface.
A family of recent methods have brought solutions to the rigid segmentation problem based on spectral clustering [3], in the context of the more general shape matching problem.

Recent surface tracking techniques over-segment the template in a set of rigid patches as initialization, then use this patch-rigid structure to constrain surface tracking [4]. An attempt to learn this segmentation during tracking has also been presented [5]. Our interest and goal in this internship is to exhibit a model similar to [4], but where the characteristics of local rigid links between patches are learned during tracking. One could for example link patches with an inter-patch spring model, whose spring rigidity parameters are to be inferred online by the estimation and tracking method. The exact nature of the local surface parameterization and the corresponding estimation algorithm are to be determined during the internship.

Development will be carried out in C++ and/or Matlab.

Keywords: rigidity, multi-view shape modeling, rigid segmentation, mass-spring model.

References

[1] A. Shamir. A survey on mesh segmentation techniques. Computer Graphics Forum, 27(6):1539–1556, 2008

[2] J. Yan and M. Pollefeys. A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, de- generate and nondegenerate. In ECCV, pages 94–106, 2006.

[3] A. Sharma, E. von Lavante, and R. Horaud. Learning shape segmentation using constrained spectral clustering and prob- abilistic label transfer. In ECCV, volume 6315, pages 743– 756, 2010.

[4] C. Cagniart, E. Boyer, and S. Ilic. Probabilistic deformable surface tracking from multiple videos. In ECCV, pages 326– 339, 2010.

[5] Franco, Jean-Sebastien; Boyer, Edmond; , Learning temporally consistent rigidities, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on , vol., no., pp.1241-1248, 20-25 June 2011