基于图的动态空间几何 - 空间几何的动态优化

Abstract

This paper explores a novel approach to space geometry optimization using graph-based techniques. Traditional methods often struggle with complex scenarios, necessitating dynamic adjustments. We introduce a framework centered around graph traversal and simplification to dynamically optimize spatial geometry, including image texture and 3D model shaping. The core mechanism leverages neural networks to learn and refine graph representations, enabling adaptive geometric transformations. This research addresses the limitations of static optimization by providing a framework for continuous, intelligent adjustment of space geometry. The proposed method offers the potential for more robust and flexible geometric modeling compared to conventional approaches.