Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The incremental rigidity scheme for structure from motion: the line-based formulation
ECCV 90 Proceedings of the first european conference on Computer vision
International Journal of Computer Vision
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-frame approach to visual motion perception
International Journal of Computer Vision
Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Form Solutions for Physically Based Shape Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovery of Nonrigid Motion and Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Shape Recovery Using Distributed Aspect Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Measurement and integration of 3-D structures by tracking edge lines
International Journal of Computer Vision
Integrating qualitative and quantitative shape recovery
International Journal of Computer Vision
Taking advantage of image-based and geometry-based constraints to recover 3-D surfaces
Computer Vision and Image Understanding
Rigidity Checking of 3D Point Correspondences Under Perspective Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algebraic Functions For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure and Motion from Line Segments in Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incorporating Illumination Constraints in Deformable Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Building Roadmaps of Minima and Transitions in Visual Models
International Journal of Computer Vision
A Framework for Model-Based Tracking Experiments in Image Sequences
International Journal of Computer Vision
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We present a model-based framework for incremental, adaptive object shape estimation and tracking in monocular image sequences. Parametric structure and motion estimation methods usually assume a fixed class of shape representation (splines, deformable superquadrics, etc.) that is initialized prior to tracking. Since the model shape coverage is fixed a priori, the incremental recovery of structure is decoupled from tracking, thereby limiting both processes in their scope and robustness. In this work, we describe a model-based framework that supports the automatic detection and integration of low-level geometric primitives (lines) incrementally. Such primitives are not explicitly captured in the initial model, but are moving consistently with its image motion. The consistency tests used to identify new structure are based on trinocular constraints between geometric primitives. The method allows not only an increase in the model scope, but also improves tracking accuracy by including the newly recovered features in its state estimation. The formulation is a step toward automatic model building, since it allows both weaker assumptions on the availability of a prior shape representation and on the number of features that would otherwise be necessary for entirely bottom-up reconstruction. We demonstrate the proposed approach on two separate image-based tracking domains, each involving complex 3D object structure and motion.