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SIGGRAPH2026顶会

Mobile3DGS3: Accelerate Mobile 3DGS Rendering via Gradient-Aware Super-Sampling and Frame Interpolation

Fan Gao, Yibo Zhao, Changhao Song, Jiarui Wen, Yuxuan Song, Youcheng Cai, Ligang Liu

2026年份

摘要

Achieving high-quality real-time rendering on mobile devices is critical for immersive 3D applications, yet 3D Gaussian Splatting (3DGS) faces significant challenges on resource-constrained mobile platforms, with sustaining 60 FPS at 2K resolution remaining difficult despite recent advances. Rather than further optimizing the core splatting pipeline, we explore 3DGS acceleration through post-rendering techniques leveraging gradient for super-sampling and frame interpolation. We propose Mobile3DGS3, a lightweight framework that alternates between super-sampling and frame interpolation. Our framework introduces three key innovations: Tangent Space Gradient Caching (TSGC), which converts gradient into cached geometric invariants enabling seamless integration with hardware-accelerated rasterization; Gradient-Aware Super-Sampling (GASS), which leverages Gaussian splat differentiability to upsample low-resolution images while preserving details; and Gradient-Aware Frame Interpolation (GAFI), which resamples gradient from adjacent frames with efficient motion vector upscaling for NPU-compatible processing. Extensive experiments demonstrate significant rendering efficiency improvements on mobile devices while maintaining high visual fidelity, presenting the first 3DGS acceleration paradigm on mobile devices that effectively leverages gradient in both super-sampling and frame interpolation. Code repository: https://github.com/SyouSanGin/Mobile3DGS3

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