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• W3140517689 abstract "In this work we investigate the problem of uncertainty estimation for image-guided depth completion. We extend Deep Basis Fitting (DBF) [54] for depth completion within a Bayesian evidence framework to provide calibrated perpixel variance. The DBF approach frames the depth completion problem in terms of a network that produces a set of low-dimensional depth bases and a differentiable least squares fitting module that computes the basis weights using the sparse depths. By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements. We conduct controlled experiments to compare BDBF against commonly used techniques for uncertainty estimation under various scenarios. Results show that our method produces better uncertainty estimates with accurate depth prediction." @default.
• W3140517689 created "2021-04-13" @default.
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• W3140517689 date "2021-10-01" @default.
• W3140517689 modified "2023-10-16" @default.
• W3140517689 title "Bayesian Deep Basis Fitting for Depth Completion with Uncertainty" @default.
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• W3140517689 doi "https://doi.org/10.1109/iccv48922.2021.01584" @default.
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