Presentation
GEMM-GS: Accelerating 3D Gaussian Splatting on Tensor Cores with GEMM-Compatible Blending
Description3D Gaussian Splatting (3DGS) has emerged as a popular representation technique for efficient novel view synthesis due to its explicit Gaussian-based formulation. However, achieving real-time rendering at 90 frames per second (FPS) remains challenging. Prior hardware accelerators have explored custom ASIC or FPGA implementations that deliver higher performance than GPUs, but these designs often rely on advanced technology nodes (e.g., 7nm) and substantial on-chip storage, which significantly increase deployment cost and limit practicality. Meanwhile, existing studies largely overlook the potential of modern GPUs, as the 3DGS pipeline lacks native General Matrix Multiplication (GEMM) operations, which are essential for utilizing Tensor Cores. In this paper, we propose GEMM-GS, a GPU acceleration framework that enables efficient 3DGS execution on Tensor Cores through a GEMM-compatible reformulation of the blending stage. GEMM-GS transforms the original blending process into matrix-multiplication-friendly operations and incorporates a high-performance CUDA kernel with a three-stage double-buffered pipeline to overlap computation and memory accesses. The design is plug-and-play on commodity GPUs, requiring no hardware modifications and thus ensuring practical deployability. Experimental results demonstrate that GEMM-GS achieves a $1.42\times$ speedup over the baseline 3DGS implementation and provides an additional $1.47\times$ average speedup when integrated with existing acceleration techniques.
Event Type
Research Manuscript
TimeWednesday, July 292:47pm - 3:00pm PDT
LocationMtg Room 203C