# SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality

Source: [SAGE: Semantic-Driven Adaptive Gaussian Splatting in Extended Reality](<https://www.sota2.com/research/paper/68fa77951e209131cea7422a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:22:38.768Z

Authors: Chiara Schiavo, Elena Camuffo, Leonardo Badia, Simone Milani

Published: 2025-03-20T23:29:24.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.16747>)

[PDF](<https://arxiv.org/pdf/2503.16747.pdf>)