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

2025-05-30

AI Summary

To address the high computational cost of conditional alpha blending and low Tensor Core utilization in 3D Gaussian Splatting (3DGS), this paper proposes TC-GS—a plug-and-play acceleration module. Methodologically, TC-GS reformulates alpha blending as a matrix multiplication operation to fully leverage GPU Tensor Core throughput; introduces a global-to-local coordinate transformation to mitigate rounding errors under half-precision arithmetic; and jointly optimizes Gaussian compression and redundancy elimination for end-to-end acceleration. Experimental results demonstrate that, while strictly preserving rendering quality, TC-GS achieves a 2.18× speedup over the state-of-the-art acceleration method and a 5.6× overall speedup versus vanilla 3DGS. This significantly enhances real-time rendering efficiency without compromising visual fidelity.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Matrix & Tensor Methods
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Data transparency and provenance

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}

Problem

• Accelerates 3D Gaussian Splatting using Tensor Cores
• Reduces rendering time via alpha-blending matrix multiplication
• Maintains quality while mitigating half-precision rounding errors

Innovation

• Utilizes Tensor Cores for 3D Gaussian Splatting
• Maps alpha computation to matrix multiplication
• Introduces global-to-local coordinate transformation