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.
神经辐射场（NeRF）在表示三维场景和合成新视图方面取得了重要进展。尽管有所进步，NeRF的高计算成本对其在资源受限环境和实时应用中的部署构成了挑战。作为一种替代NeRF类神经渲染方法，三维高斯喷溅（3DGS）提供了快速渲染速度，同时保持了优秀的图像质量。然而，由于它使用大量的高斯函数来表示对象和场景，因此需要大量的存储空间来实现高质量的表示。为了减轻存储开销，我们提出了一种新的方法——分解式三维高斯喷溅（F-3DGS），该方法大幅度降低了存储需求，同时保持了图像质量。我们的方法受到传统矩阵和张量分解技术的启发，通过高效的分解，用远少于原本数量的高斯函数来表示和近似密集的高斯团簇。我们的目标是通过对每个轴及其组合的有限信息的近似，有效地表示密集的三维高斯。这种方法使我们能够用相对较少的元素编码大量的高斯及其重要属性（如颜色、规模和旋转），这些都是渲染所必需的。广泛的实验结果表明，F-3DGS在减少存储成本的同时，保持了与渲染图像质量相当的水平。