Results 161 to 170 of about 29,746,039 (283)

Medial Axis Aware Learning of Signed Distance Functions

open access: yesComputer 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

Meshing Unsigned Distance Fields with Regular Triangulations

open access: yesComputer Graphics Forum, EarlyView.
Abstract Unsigned distance fields (UDF) are a versatile, implicit representation of geometry. They can represent surfaces that are not bounding a solid or contain points or curves that are not manifold, for example several sheets meeting along a common curve. Contouring the implicit representation, i.e. turning it into an explicit one, requires finding
M. Kohlbrenner, M. Alexa
wiley   +1 more source

SDF-1 and NOTCH signaling in myogenic cell differentiation: the role of miRNA10a, 425, and 5100. [PDF]

open access: yesStem Cell Res Ther, 2023
Mierzejewski B   +15 more
europepmc   +1 more source

Compactly supported detail field for high quality neural implicit surfaces

open access: yesComputer Graphics Forum, EarlyView.
Abstract Neural implicit surfaces are a powerful tool for encoding a surface as the zero level set of a neural function. Trained using gradient‐descent based optimizers, these methods however suffer from a low‐frequency bias that prevents them to fit fine details of the surface.
Guillaume Coiffier, Justine Basselin
wiley   +1 more source

Strictly Conservative Neural Distance Fields

open access: yesComputer Graphics Forum, EarlyView.
Abstract We propose a first method to generate neural unsigned or signed distance fields (SDFs) that are guaranteed to be conservative with respect to a given 3D shape. This means the true distance is never overestimated and the zero‐level set is a bounding volume for the shape. The method makes use of neural network architectures that ensure Lipschitz
I. Ludwig, M. Campen
wiley   +1 more source

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