Shape Reconstruction in Projective Grid Space from Large Number of Images

Hideo Saito and Takeo Kanade
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), July, 1999.


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Abstract
This paper proposes a new scheme for multi-image projective reconstruction based on a projective grid space. The projective grid space is defined by two basis views and the fundamental matrix relating these views. Given fundamental matrices relating other views to each of the two basis views, this projective grid space can be related to any view. In the projective grid space as a general space that is related to all images, a projective shape can be reconstructed from all the images of weakly calibrated cameras. The projective reconstruction is one way to reduce the effort of the calibration because it does not need Euclid metric information, but rather only correspondences of several points between the images. For demonstrating the effectiveness of the proposed projective grid definition, we modify the voxel coloring algorithm for the projective voxel scheme. The quality of the virtual view images re-synthesized from the projective shape demonstrates the effectiveness of our proposed scheme for projective reconstruction from a large number of images.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Associated Lab(s) / Group(s): Virtualized RealityTM

Text Reference
Hideo Saito and Takeo Kanade, "Shape Reconstruction in Projective Grid Space from Large Number of Images," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), July, 1999.

BibTeX Reference
@inproceedings{Saito_1999_2686,
   author = "Hideo Saito and Takeo Kanade",
   title = "Shape Reconstruction in Projective Grid Space from Large Number of Images",
   booktitle = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99)",
   month = "July",
   year = "1999",
}