Abstract
Camera pose and scene geometry estimation is a fundamental requirement for match move to insert synthetic 3D objects in real scenes. In order to automate this process, auto-calibration that estimates the camera motion without prior calibration information is needed. Most auto-calibration methods for multi-views contain bundle adjustment or non-linear minimization process that is complex and difficult problem. This paper presents two methods for recovering structure and motion from handheld image sequences: the one is key-frame selection, and the other is to reject the frame with large errors among key-frames in absolute quadric estimation by LMedS (Least Median of Square). The experimental results showed the proposed method can achieve precisely camera pose and scene geometry estimation without bundle adjustment.
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Seo, JK., Hwang, YH., Hong, HK. (2004). Structure and Motion Recovery Using Two Step Sampling for 3D Match Move. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_67
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DOI: https://doi.org/10.1007/978-3-540-24694-7_67
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