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

arXiv: https://arxiv.org/abs/2503.16747 · HTML: https://mlsanity.com/paper/2503.16747/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians](https://mlsanity.com/paper/2403.17898/) (2024)
- [Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering](https://mlsanity.com/paper/2311.17089/) (2023)
- [FLoD: Integrating Flexible Level of Detail into 3D Gaussian Splatting for Customizable Rendering](https://mlsanity.com/paper/2408.12894/) (2024)
- [Appearance-Driven Automatic 3D Model Simplification](https://mlsanity.com/paper/2104.03989/) (2021)
- [Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality](https://mlsanity.com/paper/1912.09137/) (2019)
- [DeepLab2: A TensorFlow Library for Deep Labeling](https://mlsanity.com/paper/2106.09748/) (2021)
- [Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality](https://mlsanity.com/paper/2409.15959/) (2024)
- [Convolutional Neural Networks for Continuous QoE Prediction in Video Streaming Services](https://mlsanity.com/paper/2003.08574/) (2020)

## Abstract (arXiv)

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.