3D Gaussian splatting (3D-GS) is a new rendering approach that outperforms the neural radiance field (NeRF) in terms of both speed and image quality. 3D-GS represents 3D scenes by utilizing millions of 3D Gaussians and projects these Gaussians onto the 2D image plane for rendering. However, during the rendering process, a substantial number of unnecessary 3D Gaussians exist for the current view direction, resulting in significant computation costs associated with their identification. In this paper, we propose a computational reduction technique that quickly identifies unnecessary 3D Gaussians in real-time for rendering the current view without compromising image quality. This is accomplished through the offline clustering of 3D Gaussians that are close in distance, followed by the projection of these clusters onto a 2D image plane during runtime. Additionally, we analyze the bottleneck associated with the proposed technique when executed on GPUs and propose an efficient hardware architecture that seamlessly supports the proposed scheme. For the Mip-NeRF360 dataset, the proposed technique excludes 63% of 3D Gaussians on average before the 2D image projection, which reduces the overall rendering computation by almost 38.3% without sacrificing peak-signal-to-noise-ratio (PSNR). The proposed accelerator also achieves a speedup of 10.7x compared to a GPU.

翻译：3D高斯泼溅（3D-GS）是一种新型渲染方法，在速度和图像质量上均优于神经辐射场（NeRF）。3D-GS通过利用数百万个3D高斯表征三维场景，并将这些高斯投影到二维图像平面进行渲染。然而，在渲染过程中，当前视角方向存在大量不必要的3D高斯，导致识别这些高斯产生显著的计算开销。本文提出一种计算量缩减技术，能够在不牺牲图像质量的前提下，实时快速识别当前视图渲染中不必要的3D高斯。该技术通过离线对空间邻近的3D高斯进行聚类，并在运行时将这些聚类投影到二维图像平面来实现。此外，我们分析了所提技术在GPU上执行时的瓶颈，并设计了高效硬件架构以无缝支持该方案。在Mip-NeRF360数据集上，所提技术在进行二维图像投影前平均排除了63%的3D高斯，使得整体渲染计算量降低约38.3%，且峰值信噪比（PSNR）无损失。与GPU相比，所提出的加速器实现了10.7倍的加速比。