Euclidean structure from uncalibrated images
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Stratified Self-Calibration with the Modulus Constraint
IEEE Transactions on Pattern Analysis and Machine Intelligence
Augmented Reality Using Uncalibrated Video Sequences
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Maximum Likelihood Estimation of the Template of a Rigid Moving Object
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
View generation for three-dimensional scenes from video sequences
IEEE Transactions on Image Processing
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This article describes a method to insert virtual objects into a real video stream based on feature tracking and camera pose estimation from a set of single-camera video frames. To insert or modify 3D shapes to target video frames, the transformation from the 3D objects to the projection of the objects onto the video frames should be revealed. It is shown that, without a camera calibration process, the 3D reconstruction is possible using multiple images from a single camera under the fixed internal camera parameters. The proposed approach is based on the simplification of the camera matrix of intrinsic parameters and the use of projective geometry. The method is particularly useful for augmented reality applications to insert or modify models to a real video stream. Several experimental results are presented on real-world video streams, demonstrating the usefulness of our method for the augmented reality applications.