Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Recovery of Motion and Shape in the General Case by Fixation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive-batch estimation of motion and structure from monocular image sequences
CVGIP: Image Understanding
Recursive 3-D Visual Motion Estimation Using Subspace Constraints
International Journal of Computer Vision
Reducing "Structure From Motion": A General Framework for Dynamic Vision Part 1: Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation of Scenes from Collections of Images
VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
Reducing "Structure From Motion": A General Framework for Dynamic Vision Part 1: Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Estimation of 3D Shape and Motion of Rigid Objects from Their Orthographic Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
Presence: Teleoperators and Virtual Environments
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
Continuous stereo self-calibration by camera parameter tracking
IEEE Transactions on Image Processing
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A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a "natural" dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate.