Results 131 to 140 of about 2,175,773 (278)
Differentiable Randers‐Finsler Eikonal Solvers
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
ON Λ-CONVERGENCE ALMOST EVERYWHERE OF DOUBLE FOURIER SERIES
47-49Рассматривается один вид сходимости (Λ-сходимость) двойных тригонометрических рядов Фурье, промежуточный между сходимостью по квадратам и λ-сходимостью при λ > 1.
Антонов Николай Юрьевич
core
Circles of Confidence for Multi‐Label Geometry Completion
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
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
On Bending in the As‐Rigid‐As‐Possible Deformation Energy
Abstract The well‐established As‐Rigid‐As‐Possible (ARAP) energy has various forms. For surface deformation, commonly used energies contain an implicit bending penalty. We present a natural, continuous generalization that incorporates multiple ARAP versions with an implicit, user‐controllable bending penalty.
Ugo Finnendahl, Marc Alexa
wiley +1 more source
Compactly supported detail field for high quality neural implicit surfaces
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
Tangent Blow‐Ups for Processing Non‐Manifold Geometry
Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov +3 more
wiley +1 more source
On the unconditional almost-everywhere convergence of general orthogonal series
Теореми Орлiча i Тандорi про безумовну збiжнiсть майже скрiзь щодо мiри Лебега дiйсних ортогональних рядiв, заданих на iнтервалi (0; 1), поширено на загальнi комплекснi ортогональнi ряди, що заданi на просторi з довiльною мiрою.The Orlicz and Tandori ...
Мурач, А.А. +1 more
core
The aesthetic sublime of megaproject structures: A framework and a research agenda
Abstract The physical structures of megaprojects—such as mega‐canals, metros, railway lines, bridges, tunnels, and iconic opera houses—hold a profound capacity to generate aesthetic experiences with enduring societal impact. Yet, research on megaprojects has predominantly focused on functionality and economic rationale with aesthetics being pushed to ...
Federica De Molli +2 more
wiley +1 more source
Asymptotic Regularity of a Generalised Stochastic Halpern Scheme. [PDF]
Pischke N, Powell T.
europepmc +1 more source

