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

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.

Figures (9)

• Figure 1: Mobile-GS is the first real-time Gaussian Splatting method that can reach 116 FPS rendering speed in the 1600 $\times$ 1063 resolution for Bicycle on the mobile equipped with the Snapdragon 8 Gen 3 GPU as shown in (a). We evaluate rendering quality, storage costs, and inference speed on an RTX 3090 Ti GPU in (b) and (c). Our Mobile-GS integrates depth-aware order-independent rendering, compression, and distillation techniques to deliver comparable rendering quality compared with the original 3DGS, while substantially reducing the storage requirements to 4.8 MB and achieving 1098 FPS on the unbounded scene, thereby enabling efficient deployment on mobile devices.
• Figure 2: Sorting as the primary performance bottleneck.Left: Runtime analysis of the original 3DGS highlights that the sorting operation incurs a significant computational overhead during inference. Right: Removing the sorting step substantially accelerates 3DGS, achieving several-fold speedup compared to the original implementation.
• Figure 3: Rendering pipeline of our proposed Mobile-GS compared with 3DGS. In the inference stage, different from 3DGS, our proposed method eliminates the tile-based rendering and the 3D Gaussian sorting process typically required for accurate alpha blending. Instead, we first compute the color of each 3D Gaussian for its related pixels in parallel and accumulate the color value for each pixel. Then, we composite the foreground and background color in a single pass. To further improve performance and maintain visual quality, we propose a depth-aware order-independent rendering strategy that replaces the original sorting-dependent alpha blending.
• Figure 4: Overall illustration and visualization of view-dependent opacity modeling.Left: We leverage an MLP fed with 3D Gaussian scale, rotation, spherical harmonics, and the vector of the camera toward the 3D Gaussian as input to predict a view-dependent opacity. Right: We display that our Mobile-GS removes redundant opacity and keeps important Gaussians with high opacity.
• Figure 5: Qualitative comparisons of existing methods and our proposed Mobile-GS. We display the storage cost and FPS per scene to better demonstrate the performance of our method. We extract close-ups to highlight the differences.