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The camera response function (CRF) that maps linear irradiance to pixel intensities must be known for computational imaging applications that match features in images with different exposures. This function is scene dependent and is difficult to estimate in scenes with significant motion. In this paper, we present a novel algorithm for radiometric calibration from multiple exposure images of a dynamic scene. Our approach is based on two key ideas from the literature: (1) intensity mapping functions which map pixel values in one image to the other without the need for pixel correspondences, and (2) a rank minimization algorithm for radiometric calibration. Although each method has its problems, we show how to combine them in a formulation that leverages their benefits. Our algorithm recovers the CRFs for dynamic scenes better than previous methods, and we show how it can be applied to existing algorithms such as those for high-dynamic range imaging to improve their results.

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Estimating a mesh from an unordered set of sparse, noisy 3D points is a challenging problem that requires carefully selected priors. Existing hand-crafted priors, such as smoothness regularizers, impose an undesirable trade-off between attenuating noise and preserving local detail. Recent deep-learning approaches produce impressive results by learning priors directly from the data. However, the priors are learned at the object level, which makes these algorithms class-specific, and even sensitive to the pose of the object. We introduce meshlets, small patches of mesh that we use to learn local shape priors. Meshlets act as a dictionary of local features and thus allow to use learned priors to reconstruct object meshes in any pose and from unseen classes, even when the noise is large and the samples sparse.