Resampling of multiple camera point cloud data

Even though the sampling grid of a digital camera is uniform and rectangular, the depth information reconstructed from multi-camera samples is neither uniform nor rectangular due to the rotations and translations of the camera coordinate systems with respect to each other. In order to facilitate further processing, e.g. compression of multi camera depth data, resampling of non-uniform multi-camera depth samples to a uniform rectangular grid is advantageous. In this paper, we introduce and compare two resampling methods using a voxel-based and a triangular-mesh-based approach.

[1]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[2]  Christoph Fehn,et al.  Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV , 2004, IS&T/SPIE Electronic Imaging.

[3]  Richard Szeliski,et al.  High-quality video view interpolation using a layered representation , 2004, SIGGRAPH 2004.

[4]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[5]  Richard Szeliski,et al.  A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  Aljoscha Smolic,et al.  The effects of multiview depth video compression on multiview rendering , 2009, Signal Process. Image Commun..

[7]  Thomas Wiegand,et al.  3D Video and Free Viewpoint Video - Technologies, Applications and MPEG Standards , 2006, 2006 IEEE International Conference on Multimedia and Expo.