Identification of 3D objects from multiple silhouettes using Quadtrees/Octrees
Computer Vision, Graphics, and Image Processing
Automatic Model Construction and Pose Estimation From Photographs Using Triangular Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Self-Calibration of Rotating and Zooming Cameras
International Journal of Computer Vision
Image Description and 3-D Reconstruction From Image Trajectories of Rotational Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration from Image Triplets
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Object Models from Contour Sequences
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Camera Pose Estimation and Reconstruction from Image Profiles under Circular Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Automatic 3D Model Construction for Turn-Table Sequences
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Repeated Structures: Image Correspondence Constraints and 3D Structure Recovery
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Euclidean Reconstruction from Uncalibrated Views
Proceedings of the Second Joint European - US Workshop on Applications of Invariance in Computer Vision
Metric Rectification for Perspective Images of Planes
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Geometry of single axis motions using conic fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new constraint on the imaged absolute conic from aspect ratio and its application
Pattern Recognition Letters
Silhouette Coherence for Camera Calibration under Circular Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-stage 3D reconstruction under circular motion
Image and Vision Computing
Motion Recovery for Uncalibrated Turntable Sequences Using Silhouettes and a Single Point
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Removing Pose from Face Images
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Coplanar circles, quasi-affine invariance and calibration
Image and Vision Computing
Euclidean structure from confocal conics: Theory and application to camera calibration
Computer Vision and Image Understanding
Calibration and segmentation free 3D modelling from images based on GPU
International Journal of Computer Applications in Technology
Camera Models and Fundamental Concepts Used in Geometric Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Euclidean structure from N ≥ 2 parallel circles: theory and algorithms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Camera calibration using vertical lines
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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In this paper, we describe a new approach for recovering 3D geometry from an uncalibrated image sequence of a single axis (turntable) motion. Unlike previous methods, the computation of multiple views encoded by the fundamental matrix or trifocal tensor is not required. Instead, the new approach is based on fitting a conic locus to corresponding image points over multiple views. It is then shown that the geometry of single axis motion can be recovered given at least two such conics. In the case of two conics the reconstruction may have a two fold ambiguity, but this ambiguity is removed if three conics are used.The approach enables the geometry of the single axis motion (the 3D rotation axis and Euclidean geometry in planes perpendicular to this axis) to be estimated using the minimal number of parameters. It is demonstrated that a Maximum Likelihood Estimation results in measurements that are as good as or superior to those obtained by previous methods, and with a far simpler algorithm. Examples are given on various real sequences, which show the accuracy and robustness of the new algorithm.