The neural radiance field (NeRF) has made significant strides in representing 3D scenes and synthesizing novel views. Despite its advancements, the high computational costs of NeRF have posed challenges for its deployment in resource-constrained environments and real-time applications. As an alternative to NeRF-like neural rendering methods, 3D Gaussian Splatting (3DGS) offers rapid rendering speeds while maintaining excellent image quality. However, as it represents objects and scenes using a myriad of Gaussians, it requires substantial storage to achieve high-quality representation. To mitigate the storage overhead, we propose Factorized 3D Gaussian Splatting (F-3DGS), a novel approach that drastically reduces storage requirements while preserving image quality. Inspired by classical matrix and tensor factorization techniques, our method represents and approximates dense clusters of Gaussians with significantly fewer Gaussians through efficient factorization. We aim to efficiently represent dense 3D Gaussians by approximating them with a limited amount of information for each axis and their combinations. This method allows us to encode a substantially large number of Gaussians along with their essential attributes -- such as color, scale, and rotation -- necessary for rendering using a relatively small number of elements. Extensive experimental results demonstrate that F-3DGS achieves a significant reduction in storage costs while maintaining comparable quality in rendered images.

翻译：神经辐射场（NeRF）在表示3D场景和合成新视角方面取得了显著进展。尽管NeRF技术不断进步，但其高昂的计算成本为其在资源受限环境和实时应用中的部署带来了挑战。作为类NeRF神经渲染方法的替代方案，3D高斯溅射（3DGS）在保持优异图像质量的同时提供了快速的渲染速度。然而，由于它使用大量高斯函数来表示物体和场景，实现高质量表示需要巨大的存储开销。为减轻存储负担，我们提出了因子化3D高斯溅射（F-3DGS），这是一种在保持图像质量的同时大幅降低存储需求的新方法。受经典矩阵和张量分解技术的启发，我们的方法通过高效分解，用少得多的高斯函数来表示和近似密集的高斯簇。我们的目标是通过为每个坐标轴及其组合使用有限的信息进行近似，从而高效地表示密集的3D高斯函数。该方法使我们能够用相对较少的元素编码大量高斯函数及其渲染所需的关键属性——例如颜色、尺度和旋转。大量实验结果表明，F-3DGS在保持渲染图像质量相当的同时，显著降低了存储成本。