Global Parameterization-based Texture Space Optimization

TL;DR

This paper introduces a global parameterization-based method to optimize texture space, making texture mapping more compact, efficient, and reducing computational costs in graphics rendering.

Contribution

It presents a novel, robust, and efficient approach to produce a tight texture space, improving storage and rendering performance over existing methods.

Findings

Enhanced storage efficiency in texture mapping

Reduced computational cost during texture optimization

Improved rendering performance with compact texture spaces

Abstract

Texture mapping is a common technology in the area of computer graphics, it maps the 3D surface space onto the 2D texture space. However, the loose texture space will reduce the efficiency of data storage and GPU memory addressing in the rendering process. Many of the existing methods focus on repacking given textures, but they still suffer from high computational cost and hardly produce a wholly tight texture space. In this paper, we propose a method to optimize the texture space and produce a new texture mapping which is compact based on global parameterization. The proposed method is computationally robust and efficient. Experiments show the effectiveness of the proposed method and the potency in improving the storage and rendering efficiency.