3D Gaussian Splatting (3DGS) has significantly improved the efficiency and realism of three-dimensional scene visualization in several applications, ranging from robotics to eXtended Reality (XR). This work presents SAGE (Semantic-Driven Adaptive Gaussian Splatting in Extended Reality), a novel framework designed to enhance the user experience by dynamically adapting the Level of Detail (LOD) of different 3DGS objects identified via a semantic segmentation. Experimental results demonstrate how SAGE effectively reduces memory and computational overhead while keeping a desired target visual quality, thus providing a powerful optimization for interactive XR applications.
View on arXiv@article{schiavo2025_2503.16747,
title={ SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality },
author={ Chiara Schiavo and Elena Camuffo and Leonardo Badia and Simone Milani },
journal={arXiv preprint arXiv:2503.16747},
year={ 2025 }
}