Metric 3D Surface Reconstruction from Uncalibrated Image Sequences

Modeling of 3D objects from image sequences is one of the challenging problems in computer vision and has been a research topic for many years. Important theoretical and algorithmic results were achieved that allow to extract even complex 3D scene models from images. One recent effort has been to reduce the amount of calibration and to avoid restrictions on the camera motion. In this contribution an approach is described which achieves this goal by combining state-of-the-art algorithms for uncalibrated projective reconstruction, self-calibration and dense correspondence matching.

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