← 研踪 PaperTrailCVF · CVPR 2026:cvf:05f7f8f0cdd11364590e405a77f08cbb · 记录 #1
Seele: A Unified Acceleration Framework for Real-Time Gaussian Splatting on Mobile Devices
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:31
摘要
3D Gaussian Splatting (3DGS) has become a crucial rendering technique for many real-time applications. How- ever, the limited hardware resources on today's mobile platforms hinder these applications, as they struggle to achieve real-time performance. In this paper, we propose SEELE, a general framework designed to accelerate the 3DGS pipeline for resource-constrained mobile devices. Specifically, we propose two GPU-oriented techniques: hybrid preprocessing and contribution-aware rasterization. Hybrid preprocessing alleviates the GPU compute and memory pressure by reducing the number of irrelevant Gaussians during rendering. The key is to combine our view-dependent scene representation with online filtering. Meanwhile, contribution-aware rasterization improves the GPU utilization at the rasterization stage by prioritizing Gaussians with high contributions while reducing computations for those with low contributions. Both techniques can be seamlessly integrated into existing 3DGS pipelines with minimal fine-tuning. Collectively, our framework achieves up to 6.3x speedup and 39.1% model reduction while achieving superior rendering quality compared to existing methods. Our codes will be released upon publication.
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记录 #1 · 2026 · 最新观测版本
Seele: A Unified Acceleration Framework for Real-Time Gaussian Splatting on Mobile Devices
3D Gaussian Splatting (3DGS) has become a crucial rendering technique for many real-time applications. How- ever, the limited hardware resources on today's mobile platforms hinder these applications, as they struggle to achieve real-time performance. In this paper, we propose SEELE, a general framework designed to accelerate the 3DGS pipeline for resource-constrained mobile devices. Specifically, we propose two GPU-oriented techniques: hybrid preprocessing and contribution-aware rasterization. Hybrid preprocessing alleviates the GPU compute and memory pressure by reducing the number of irrelevant Gaussians during rendering. The key is to combine our view-dependent scene representation with online filtering. Meanwhile, contribution-aware rasterization improves the GPU utilization at the rasterization stage by prioritizing Gaussians with high contributions while reducing computations for those with low contributions. Both techniques can be seamlessly integrated into existing 3DGS pipelines with minimal fine-tuning. Collectively, our framework achieves up to 6.3x speedup and 39.1% model reduction while achieving superior rendering quality compared to existing methods. Our codes will be released upon publication.
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