Results 121 to 130 of about 2,263,118 (235)
Metric uniform distribution on analytic curves: With an Appendix by Jim Wright. [PDF]
Res Math SciBergelson V, Moreira J.
europepmc +1 more source
Real‐time by‐example texture synthesis and filtering using local statistics exchange
Computer Graphics Forum, EarlyView.Abstract Real‐time by‐example texture synthesis is used in interactive virtual worlds to generate the appearance of an unbounded surface from an exemplar texture with as few repetitions as possible. Currently, leading real‐time methods rely on a tiling and blending scheme which is known to synthesize well texture patterns with little spatial ...
Nicolas Lutz, Guillaume Gilet
wiley +1 more source
Asymptotic Regularity of a Generalised Stochastic Halpern Scheme. [PDF]
J Optim Theory ApplPischke N, Powell T.
europepmc +1 more source
Differentiable Randers‐Finsler Eikonal Solvers
Computer Graphics Forum, EarlyView.Abstract Fast and differentiable solvers for anisotropic and asymmetric distance fields are a key primitive in geometry processing, enabling gradient‐based optimization over metrics, drift fields, and downstream objectives that depend on geodesic distances and geodesics.
Barak Gahtan +2 more
wiley +1 more source
A New Estimator of Kullback-Leibler Divergence via Shannon Entropy. [PDF]
Entropy (Basel)Çadırcı MS, Singull M.
europepmc +1 more source
Circles of Confidence for Multi‐Label Geometry Completion
Computer Graphics Forum, EarlyView.Abstract Inside–outside classification is widely used for geometry processing tasks such as surface reconstruction, geometry completion, and calculating signed distance fields. We introduce a new integral formulation of this problem, which assigns confidence scores that points are inside or outside, given incomplete boundary geometry.
Z. Wei +4 more
wiley +1 more source
Medial Axis Aware Learning of Signed Distance Functions
Computer Graphics Forum, EarlyView.Abstract We propose a novel variational method to compute a highly accurate global signed distance function (SDF) to a given point cloud. To this end, the jump set of the gradient of the SDF, which coincides with the medial axis of the surface, is explicitly taken into account through a higher‐order variational formulation that enforces linear growth ...
Samuel Weidemaier +2 more
wiley +1 more source
Random Neural Networks for Rough Volatility. [PDF]
Appl Math OptimJacquier A, Žurič Ž.
europepmc +1 more source
The three-dimensional Gauss algorithm is strongly convergent almost everywhere
, 2002 A proof that the three-dimensional Gauss algorithm is strongly convergent almost everywhere is given. This algorithm is equivalent to Brun's algorithm and to the modified Jacobi-Perron algorithm considered by Podsypanin and Schweiger.
Hardcastle, D M
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