IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D interpretation of optical flow by renormalization
International Journal of Computer Vision
Linear subspace methods for recovering translational direction
Proceedings of the 1991 York conference on Spacial vision in humans and robots
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Stereo-Motion with Stereo and Motion in Complement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Matching Using Epipolar Geometry
IEEE Transactions on Pattern Analysis and Machine Intelligence
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
3-D Translational Motion and Structure from Binocular Image Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Multiview Reconstruction of Space Curves
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Estimation of 3D Motion and Surface Structure from Local Affine Flow Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-From-Silhouette Across Time Part I: Theory and Algorithms
International Journal of Computer Vision
Robust Structure and Motion from Outlines of Smooth Curved Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouette-based 3-D model reconstruction from multiple images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On 3-D scene flow and structure recovery from multiview image sequences
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Statistical bias in 3-D reconstruction from a monocular video
IEEE Transactions on Image Processing
Automatic reconstruction of stationary 3-D objects from multiple uncalibrated camera views
IEEE Transactions on Circuits and Systems for Video Technology
Robust estimation of rigid-body 3-D motion parameters based on point correspondences
IEEE Transactions on Circuits and Systems for Video Technology
Geometric algorithms for least squares estimation of 3-D information from monocular image
IEEE Transactions on Circuits and Systems for Video Technology
Constructing 3D motions from curvature and torsion profiles
Computer-Aided Design
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This paper proposes a robust curve based method to reconstruct 3D model of an object from image sequences captured by two perpendicular stereo rigs. First, corresponding points and the geometry of points are computed in stereo images by extracting unique space curves. A new algorithm is proposed to extract unique space curves from plane curves in stereo images based on curvature and torsion consistency. The proposed method provides accurate geometry of the curve points with extremely reduced number of outliers. Contrarily to the standard sparse approaches that need sub-pixel accuracy to compute structure and motion, the proposed curve matching method deals with pixel accuracy information. More importantly, it finds the correspondence based on curve shape and does not use any photometric information. This property makes the matching process very robust against the color and intensity maladjustment of stereo rigs. Second, the recovered space curves are employed to estimate robust motion by minimizing the curve distance in the next sequence of stereo images. An efficient structure of stereo rigs - perpendicular double stereo - is proposed to improve accuracy of motion estimation. We discuss and prove its properties mathematically. Third, a set of calibrated virtual cameras are constructed from estimated motion information to take advantage of the shape-from-silhouette using multiple views and extract the object's visual hull as fine as possible. As a whole, a complete automatic and practical system of three-dimensional modeling from raw images captured by calibrated perpendicular double stereo rigs to surface representation is proposed. Fine motion estimation is the main advantage of the proposed method using perpendicular stereo rigs, which makes use of the space curves in a large base line camera setup. While the previous methods of motion estimation suffer from the statistical bias due to quantization noise, measurement error, and outliers in the input data set, the proposed method overcomes the bias problem even in pixel-level information. Experimental results demonstrate the privileged performance of the proposed method for a variety of object shapes and textures.