Mobile-GS: Real-time Gaussian Splatting for Mobile Devices

2026-03-12

AI Summary

This work addresses the challenge of deploying 3D Gaussian splatting on mobile devices, where its substantial computational and memory demands hinder real-time performance. To overcome this limitation, the authors propose an efficient, mobile-oriented Gaussian splatting method that integrates several key innovations: depth-aware order-independent rendering, neural view-dependent enhancement, first-order spherical harmonics distillation, neural vector quantization, and contribution-based Gaussian pruning. Together, these techniques yield a compact yet high-fidelity representation that significantly reduces model size and computational load while preserving visual quality. The proposed approach achieves, for the first time, real-time Gaussian splatting rendering on resource-constrained mobile platforms without compromising perceptual fidelity.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of applications.However, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices. Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering. Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. In this way, Mobile-GS can achieve both high-quality and real-time rendering. Furthermore, to facilitate deployment on memory-constrained mobile platforms, we also introduce first-order spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.

Problem

• 3D Gaussian Splatting
• mobile devices
• real-time rendering
• computational bottleneck
• memory constraints

Innovation

• Gaussian Splatting
• order-independent rendering
• view-dependent enhancement
• neural vector quantization
• mobile real-time rendering