A Survey of Spline-based Volumetric Data Modeling Framework and Its Applications

TL;DR

This paper surveys spline-based volumetric data modeling frameworks, highlighting their advantages over traditional surface models, and discusses their applications and unresolved challenges in representing complex 3D solids efficiently.

Contribution

It provides a comprehensive survey of spline-based volumetric modeling methods and explores their potential in various applications, addressing key challenges in the field.

Findings

Spline-based models effectively represent interior space and materials.

Current methods face challenges with complex geometry and large-scale data.

Spline frameworks enable advanced 3D modeling applications.

Abstract

The rapid advances in 3D scanning and acquisition techniques have given rise to the explosive increase of volumetric digital models in recent years. This dissertation systematically trailblazes a novel volumetric modeling framework to represent 3D solids. The need to explore more efficient and robust 3D modeling framework has gained the prominence. Although the traditional surface representation (e.g., triangle mesh) has many attractive properties, it is incapable of expressing the interior space and materials. Such a serious drawback overshadows many potential modeling and analysis applications. Consequently volumetric modeling techniques become the well-known solution to this problem. Nevertheless, many unsolved research issues remain when developing an efficient modeling paradigm for existing 3D models: complex geometry (fine details and extreme concaveness), arbitrary topology, heterogenous materials, large-scale data storage and processing, etc.