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- W4299680071 abstract "In this paper, we use known camera motion associated to a video sequence of a static scene in order to estimate and incrementally refine the surrounding depth field. We exploit the SO(3)-invariance of brightness and depth fields dynamics to customize standard image processing techniques. Inspired by the Horn-Schunck method, we propose a SO(3)-invariant cost to estimate the depth field. At each time step, this provides a diffusion equation on the unit Riemannian sphere that is numerically solved to obtain a real time depth field estimation of the entire field of view. Two asymptotic observers are derived from the governing equations of dynamics, respectively based on optical flow and depth estimations: implemented on noisy sequences of synthetic images as well as on real data, they perform a more robust and accurate depth estimation. This approach is complementary to most methods employing state observers for range estimation, which uniquely concern single or isolated feature points." @default.
- W4299680071 created "2022-10-02" @default.
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- W4299680071 date "2011-03-13" @default.
- W4299680071 modified "2023-09-26" @default.
- W4299680071 title "SO(3)-invariant asymptotic observers for dense depth field estimation based on visual data and known camera motion" @default.
- W4299680071 doi "https://doi.org/10.48550/arxiv.1103.2539" @default.
- W4299680071 hasPublicationYear "2011" @default.
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