Frame grouping measure for factorization-based projective reconstruction

The factorization-based method generally suffers less from drift and error accumulation than the merging. However, the factorization method assumes that all correspondences must remain in all frames. In order to overcome the limitation, we present a new factorization-based projective reconstruction...

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Published inProceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004 Vol. 4; pp. 112 - 115 Vol.4
Main Authors Yoon-Yong Jung, Yong-Ho Hwang, Hyun-Ki Hong
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Calibration
Cameras
Computer vision
Geometry
Image reconstruction
Image sequences
Layout
Merging
Pixel
Virtual environment
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Summary:The factorization-based method generally suffers less from drift and error accumulation than the merging. However, the factorization method assumes that all correspondences must remain in all frames. In order to overcome the limitation, we present a new factorization-based projective reconstruction from un-calibrated image sequences. The proposed method breaks the full sequence into sub-sequences based on a quantitative measure considering the number of matching points between frames, the homography error, and the distribution of matching points in the image. All of projective reconstructions in sub-sequences are registered into the same coordinate frame for a complete description of the scene. Experimental results showed our algorithm could recover more precise 3D structure than the merging method.
ISBN:0769521282
9780769521282
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2004.1333718