WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Recursive non-rigid structure from motion with online learned shape prior
Computer Vision and Image Understanding
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In the sequential approach to three-dimensional reconstruction, adding prior knowledge about camera pose improves reconstruction accuracy. We add a smoothing penalty on the camera trajectory. The smoothing parameter, usually fixed by trial and error, is automatically estimated using Cross-Validation. This technique is extremely expensive in its basic form. We derive Gauss-Newton Cross-Validation, which closely approximates Cross-Validation, while being much cheaper to compute. The method is substantiated by experimental results on synthetic and real data. They show that it improves accuracy and stability in the reconstruction process, preventing several failure cases.