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Found 8 papers, 1 papers with code
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation
To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs.
Learning to Separate Multiple Illuminants in a Single Image
We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each individual illuminant.
Reflectance Capture Using Univariate Sampling of BRDFs
A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF.
Illuminant Spectra-based Source Separation Using Flash Photography
Real-world lighting often consists of multiple illuminants with different spectra.
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection
We compared linear (PCA and KPCA), manifold (LPP and LLE), supervised (LDA and KDA) and hybrid approaches (LSDA) to DR with respect to AU detection.
Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars
At the core of our techniques is the assumption that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary. This assumption enables a per-pixel surface normal and BRDF estimation framework that is computationally tractable and requires no initialization in spite of the underlying problem being non-convex.
A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance
We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF.
Transitive Distance Clustering with K-Means Duality
We propose a very intuitive and simple approximation for the conventional spectral clustering methods.
