Identifying Unnecessary 3D Gaussians using Clustering for Fast Rendering of 3D Gaussian Splatting
**Identifying Unnecessary 3D Gaussians using Clustering for Fast Rendering of 3D Gaussian Splatting
基于聚类的三维高斯散射快速绘制算法**
Joongho Jo, Hyeongwon Kim, Jongsun Park
Joongho Jo，Hyeongwon Kim，Jongsun Park
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.
3D高斯散射（3D-GS）是一种新的绘制方法，在速度和图像质量方面都优于神经辐射场（NeRF）。3D-GS通过利用数百万个3D高斯来表示3D场景，并将这些高斯投影到2D图像平面上进行渲染。然而，在渲染过程中，对于当前视图方向存在大量不必要的3D高斯，导致与它们的识别相关联的显著计算成本。在本文中，我们提出了一种计算减少技术，快速识别不必要的3D高斯实时渲染当前视图，而不影响图像质量。这是通过对距离较近的3D高斯进行离线聚类，然后在时将这些聚类投影到2D图像平面上来实现的。 此外，我们分析了所提出的技术在GPU上执行时的瓶颈，并提出了一个高效的硬件架构，无缝支持所提出的方案。对于Mip-NeRF 360数据集，所提出的技术在2D图像投影之前平均排除了63%的3D高斯，这在不牺牲峰值信噪比（PSNR）的情况下减少了近38.3%的整体渲染计算。与GPU相比，该加速器还实现了10.7倍的加速比。