Continuous Mesh Framework Part II: Validations and Applications

Continuous Mesh Framework Part I: Well-Posed Continuous Interpolation Error

In the context of mesh adaptation, Riemannian metric spaces have been used to prescribe orientation, density and stretching of anisotropic meshes. But, such structures are only considered to compute lengths in adaptive mesh generators. In this article, a Riemannian metric space is shown to be more than a way to compute a length. It is proven to be a reliable continuous mesh model. In particular...

A Unified Framework for Mesh Segmentation and Part Annotation

This paper proposed a unified framework to generate consistent segmentation and annotate part semantic for 3D mesh simultaneously. The core idea of the proposed framework is to bridge the semantic gap between low-level geometry feature and high-level semantic feature by structure ontology. The structure ontology makes a consistence representation for mesh structure, geometry feature, semantic c...

Geometric algebra: a computational framework for geometrical applications (part II: applications)

Geometric algebra is a consistent computational framework in which to define geometric primitives and their relationships. This algebraic approach contains all geometric operators and permits coordinate-free specification of computational constructions. It contains primitives of any dimensionality (rather than just vectors). This second paper on the subject uses the basic products to represent ...

On continuous convergence and epi-convergence of random functions. Part II: Sufficient conditions and applications

Part II of the paper aims at providing conditions which may serve as a bridge between existing stability assertions and asymptotic results in probability theory and statistics. Special emphasis is put on functions that are expectations with respect to random probability measures. Discontinuous integrands are also taken into account. The results are illustrated applying them to functions that re...

Part II CONTINUOUS TIME STOCHASTIC PROCESSES