ICPR2014KinectVarietyFusion

Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation

Mansi Sharma, Santanu Chaudhury, and Brejesh Lall

In Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), 2014

Abstract

This paper presents a novel low-cost hybrid Kinect-variety based content generation scheme for 3DTV displays. The integrated framework constructs an efficient consistent image-space parameterization of 3D scene structure using only sparse depth information of few reference scene points. Under full-perspective camera model, the enforced Euclidean constraints simplify the synthesis of high quality novel multiview content for distinct camera motions. The algorithm does not rely on complete precise scene geometry information, and are unaffected by scene complex geometric properties, unconstrained environmental variations and illumination conditions. It, therefore, performs fairly well under a wider set of operation condition where the 3D range sensors fail or reliability of depth-based algorithms are suspect. The robust integration of vision algorithm and visual sensing scheme complement each other’s shortcomings. It opens new opportunities for envisioning vision-sensing applications in uncontrolled environments. We demonstrate that proposed robust integration provides guarantees on the completeness and consistency of the algorithm. This leads to improved reliability on an extensive set of experimental results.

ICPR2014CRpaper.pdf

ICPR2014poster.pdf

Citation

Mansi Sharma, Santanu Chaudhury, Brejesh Lall. Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation. 2014 22nd International Conference on Pattern Recognition, Stockholm, 2014, pp. 2275-2280. doi: 10.1109/ICPR.2014.395

BibTeX

@INPROCEEDINGS{6977107,

author={M. Sharma and S. Chaudhury and B. Lall},

booktitle={2014 22nd International Conference on Pattern Recognition},

title={Kinect-Variety Fusion: A Novel Hybrid Approach for Artifacts-Free 3DTV Content Generation},

year={2014},

pages={2275-2280},

doi={10.1109/ICPR.2014.395},

ISSN={1051-4651},

month={Aug}

}