  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.
