用张量核心加速3D高斯点渲染，速度提升5.6倍且兼容现有框架。
TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
- 将透明度计算转为矩阵乘法，激活闲置的张量核心
- 在保持画质前提下，比现有加速算法快2.18倍，总提速达5.6倍
- 适配主流3DGS框架，适合追求渲染效率的研究者
3D高斯点渲染通过光栅化高斯原语生成像素，其中条件α混合占渲染管线的大部分计算开销。本文提出TC-GS，一种与算法无关的通用模块，扩展了张量核心（TCU）在3DGS中的应用，实现显著加速并可无缝集成到现有3DGS优化框架中。核心创新在于将α计算映射为矩阵乘法，充分利用现有3DGS实现中闲置的张量核心。TC-GS可即插即用，与高斯压缩、冗余消除等渲染优化算法无缝结合。此外，我们引入全局到局部坐标变换，缓解张量核心半精度计算中由像素坐标二次项引起的舍入误差。大量实验表明，本方法在保持渲染质量的同时，相比现有高斯加速算法额外获得2.18倍加速，总计最高达5.6倍加速。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the computational cost in the rendering pipeline. This paper proposes TC-GS, an algorithm-independent universal module that expands the applicability of Tensor Core (TCU) 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 and integrates seamlessly with rendering pipeline designs, such as 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, thereby achieving a total acceleration of up to 5.6x.
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