Spacetime stereo: A unifying framework for depth from triangulation

Honda Research Institute, Mountain View, CA 94041, USA.
IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 5.78). 03/2005; 27(2):296-302. DOI: 10.1109/TPAMI.2005.37
Source: PubMed


Depth from triangulation has traditionally been investigated in a number of independent threads of research, with methods such as stereo, laser scanning, and coded structured light considered separately. In this paper, we propose a common framework called spacetime stereo that unifies and generalizes many of these previous methods. To show the practical utility of the framework, we develop two new algorithms for depth estimation: depth from unstructured illumination change and depth estimation in dynamic scenes. Based on our analysis, we show that methods derived from the spacetime stereo framework can be used to recover depth in situations in which existing methods perform poorly.

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    • "Les reconstructions initiales sont ensuite corrigées grâce à une table de correspondence précalculée de manière analytique qui permet de corriger l'erreur d'estimation de phase. Les approches spatiotemporelles [13] [1] cherchent à valider dans le temps les observations spatiales. Une vision globale plus exhaustive des méthodes de stéréo active est donnée par Salvi et al. [7]. "
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    • "The third group is composed of methods of spatiotemporal stereo that do not estimate the motion explicitly , but exploit a local spatiotemporal neighbourhood of pixels to increase discriminability of the similarity statistics. Paper [5] projects an artificial pattern varying over time onto the static scene and temporally aggregate the statistic. The similarity statistic (based on bilateral filtering) is temporally aggregated also in [9], such that adjacent frames are weighted by a Gaussian kernel to cope with a small motion. "
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    Preview · Conference Paper · Nov 2012
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    • "That would be true especially when the projected video contains a lot of texture allowing for simple greedy methods to work. Such methods are known as spatio-temporal stereovision [2] [23]. "
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