A Spline-based Volumetric Data Modeling Framework and Its Applications

TL;DR

This paper introduces a spline-based framework for converting surface meshes into tensor-product trivariate splines, enabling compact, continuous volumetric data representation suitable for various physical and geometric applications.

Contribution

It presents a novel methodology for converting conventional data into tensor-product splines, facilitating seamless integration into industry standards for volumetric modeling.

Findings

Enables compact and continuous volumetric data representation.

Supports applications like mechanical analysis and virtual surgery training.

Integrates seamlessly with existing industry tools.

Abstract

In this dissertation, we concentrate on the challenging research issue of developing a spline-based modeling framework, which converts the conventional data (e.g., surface meshes) to tensor-product trivariate splines. This methodology can represent both boundary/volumetric geometry and real volumetric physical attributes in a compact and continuous fashion. The regular tensor-product structure enables our new developed methods to be embedded into the industry standard seamlessly. These properties make our techniques highly preferable in many physically-based applications including mechanical analysis, shape deformation and editing, virtual surgery training, etc.