Projective Reconstruction with Missing Data
نویسنده
چکیده
An application of the KL procedure for gappy data is presented to extend the well known factorization based algorithm for multi-image projective structure and motion. The predominant problem with factorization based techniques is that they require all points being reconstructed to be visible in all views, which is very unlikely in real scenes due to self occlusion. The approach presented here attempts to estimate the missing entries in the rescaled measurement matrix by enforcing it’s rank-4 constraint. Once the missing entries have been estimated, factorization into structure and motion can be computed as usual using Singular Value Decomposition.
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