Compensated Visual Hull with GPU-Based Optimization

We propose an advanced visual hull technique to compensate for outliers using the reliabilities of silhouettes. The proposed method consists of a foreground extraction technique with multiple thresholds based on the Generalized Gaussian Family model and a compensated visual hull algorithm. We proved that the proposed technique constructs a compact visual hull even in the presence of segmentation errors and occlusions. The 3D reconstruction and rendering processes are implemented on a graphics processing unit to greatly accelerate computation time.

[1]  Luc Van Gool,et al.  Blue-c: a spatially immersive display and 3D video portal for telepresence , 2003, IPT/EGVE.

[2]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[3]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Hans-Peter Seidel,et al.  Improved Hardware-Accelerated Visual Hull Rendering , 2003, VMV.

[5]  Marc Pollefeys,et al.  Visual Hull Construction in the Presence of Partial Occlusion , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[6]  Steve Mann,et al.  OpenVIDIA: parallel GPU computer vision , 2005, ACM Multimedia.

[7]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[8]  Marc Pollefeys,et al.  Visual-hull reconstruction from uncalibrated and unsynchronized video streams , 2004, Proceedings. 2nd International Symposium on 3D Data Processing, Visualization and Transmission, 2004. 3DPVT 2004..

[9]  Rin-ichiro Taniguchi,et al.  Real-Time Free-Viewpoint Video Generation Using Multiple Cameras and a PC-Cluster , 2004, PCM.

[10]  Itaru Kitahara,et al.  Robust foreground extraction technique using background subtraction with multiple thresholds , 2007 .

[11]  Trevor Darrell,et al.  A Bayesian approach to image-based visual hull reconstruction , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  Berna Erol,et al.  A Bayesian framework for Gaussian mixture background modeling , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  A. K. Nandi,et al.  Maximum likelihood parameter estimation of the asymmetric generalised Gaussian family of distributions , 1999, Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99.

[14]  Katsushi Ikeuchi,et al.  Microfacet Billboarding , 2002, Rendering Techniques.

[15]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[16]  Kiyoharu Aizawa,et al.  Advances in Multimedia Information Processing - PCM 2004, 5th Pacific Rim Conference on Multimedia, Tokyo, Japan, November 30 - December 3, 2004, Proceedings, Part I , 2005, Pacific Rim Conference on Multimedia.

[17]  Takeo Kanade,et al.  Historical Perspectives on 4D Virtualized Reality , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[18]  Fatih Porikli,et al.  Human Body Tracking by Adaptive Background Models and Mean-Shift Analysis , 2003 .

[19]  Xiaojun Wu,et al.  Real-time dynamic 3-D object shape reconstruction and high-fidelity texture mapping for 3-D video , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Itaru Kitahara,et al.  Robust Silhouette Extraction Technique Using Background Subtraction , 2007 .