TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores

• URL: http://arxiv.org/abs/2505.24796v1
• Date: Fri, 30 May 2025 16:58:18 GMT
• Title: TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
• Authors: Zimu Liao, Jifeng Ding, Rong Fu, Siwei Cui, Ruixuan Gong, Li Wang, Boni Hu, Yi Wang, Hengjie Li, XIngcheng Zhang, Hui Wang,
• Abstract summary: This paper proposes TC-GS, an algorithm-independent universal module that expands Core (TCU) applicability for 3DGS.<n>The key innovation lies in mapping alpha to matrix multiplication, fully utilizing otherwise idle TCUs in existing 3DGS implementations.
• Score: 9.744829716477627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the time cost in the rendering pipeline. This paper proposes TC-GS, an algorithm-independent universal module that expands Tensor Core (TCU) applicability for 3DGS, leading to substantial speedups and seamless integration into existing 3DGS optimization frameworks. The key innovation lies in mapping alpha computation to matrix multiplication, fully utilizing otherwise idle TCUs in existing 3DGS implementations. TC-GS provides plug-and-play acceleration for existing top-tier acceleration algorithms tightly coupled with rendering pipeline designs, like Gaussian compression and redundancy elimination algorithms. Additionally, we introduce a global-to-local coordinate transformation to mitigate rounding errors from quadratic terms of pixel coordinates caused by Tensor Core half-precision computation. Extensive experiments demonstrate that our method maintains rendering quality while providing an additional 2.18x speedup over existing Gaussian acceleration algorithms, thus reaching up to a total 5.6x acceleration. The code is currently available at anonymous \href{https://github.com/TensorCore3DGS/3DGSTensorCore}

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