Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
International Journal of Robotics Research
3D point of regard and subject motion from a portable video-based monocular eye tracker
ACM SIGGRAPH 2007 posters
ACM Transactions on Applied Perception (TAP)
Distance measurement in panoramic video
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Scene reconstruction and visualization from internet photo collections
Scene reconstruction and visualization from internet photo collections
Determining an initial image pair for fixing the scale of a 3d reconstruction from an image sequence
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A domain reduction algorithm for incremental projective reconstruction
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Vision-based augmented reality visual guidance with keyframes
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Automatic camera calibration and scene reconstruction with scale-invariant features
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A semi-interactive panorama based 3D reconstruction framework for indoor scenes
Computer Vision and Image Understanding
3D face and motion estimation from sparse points using adaptive bracketed minimization
Multimedia Tools and Applications
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In this paper; we present an approach that is able to reconstruct 3D models from extended video sequences captured with an uncalibrated hand-held camera. We focus on two specific issues: (1) key-frame selection, and ( 2 ) projective drift. Given a long video sequence it is often not practical to work with all videoframes. In addition, to allow for effective outlier rejection and motion estimation it is necessary to have a sufficient baseline between frames. For this purpose, we propose a key-frame selection procedure based on a robust model selection criterion. Our approach guarantees that the camera motion can be estimated reliably by analyzing the feature correspondences between three consecutive views. Another problem for long uncalibrated video sequences is projective drift. Error accumulation leads to a non-projective distortion of the model. This causes the projective basis at the beginning and the end of the sequence to become inconsistent and leads to the failure of self-calibration. We propose a self-calibration approach that is insensitive to this global projective drift. Afterself-calibration triplets of key-frames are aligned using absolute orientation and hierarchically merged into a complete metric reconstruction. Next, we compute a detailed 3D surface model using stereo matching. The 3D model is textured using some of the frames.