Robust recovery of the epipolar geometry for an uncalibrated stereo rig
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Calibration-Free Augmented Reality
IEEE Transactions on Visualization and Computer Graphics
Structure from Motion Causally Integrated Over Time
IEEE Transactions on Pattern Analysis and Machine Intelligence
Euclidean Reconstruction from Uncalibrated Views
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Autocalibration and the absolute quadric
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Factorization Methods for Projective Structure and Motion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Euclidean Reconstruction from Constant Intrinsic Parameters
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
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In this paper, we propose a new camera calibration method for the 3D-based image synthesis and 3D reconstruction. We improve the problem as changing the principle point for obtaining the linear equation. According to the error rate, we adapt the non-linear method that minimizes the intrinsic parameters. Namely, it minimizes the intrinsic parameters error with maintaining the computational conciseness. As a result, we can find optimized camera intrinsic parameters and adapt to image synthesis and reconstruction. Experimental results show the performance of the proposed method is the better than the previous. We also demonstrate examples of the 3D-based image synthesis and 3D reconstruction from uncalibrated images.