Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
3-D Motion and Structure from 2-D Motion Causally Integrated over Time: Implementation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Model Selection and Surface Merging in Reconstruction Algorithms
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a novel approach to camera motion parametrization for the structure and motion problem. In a model-based framework, the hypothesis of (relatively) continuous and smooth sensor motion enables to reformulate the motion recovery problem as a global curve estimation problem on the camera path. Curves of incremental complexity are fitted using model selection to take into account incoming image data. No first estimate guess is needed. The use of modeling curves lead to a meaningful description of the camera trajectories, with a drastic reduction in the number of degrees of freedom. In order to characterize the behaviour and performances of the approach, experiments with various long video sequences, both synthetic and real, are undertaken. Several candidate curve models for motion estimation are presented and compared, and the results validate the work in terms of reconstruction accuracy, noise robustness and model compacity.