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.

通过矩阵和张量分解技术，我们提出了一种新方法，即因式化三维高斯粒子点描（F-3DGS），通过高效的因式化，大大减少了存储需求，同时保持了渲染图像的质量。