F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting

Abstract

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.

Factorized coordinates in 3DGS

Examples of factorized coordinates: (a) 25 normal coordinates, (b) 5 × 5 factorized coordinates. each x and y axis has 5 points, and both represent 25 (5 × 5) points. (c) two 5 × 5 factorized coordinates and a total of 50 points are represented (2 × 5 × 5), (d) multi-resolution factorized coordinates, where two factorized coordinates have different resolutions (3 × 3 and 5 × 5), represent total 34 points, (e) two 3 × 3 and one 5 × 5 factorized coordinates. A total of 43 points are represented. The best-viewed in color.

Illustration of factorized coordinates and representations. p, s, q, and f denote coordinate, scale, rotation (in quaternion), and features for color and opacities, respectively. The lower indices of s, q, and f are the axis and the indices of the feature dimension. For element-wise multiplication, we used the ⊙ notation.

Visualization of F-3DGS and 3DGS

These visualize Gaussian points, ellipsoids, and rendered images of six objects. We present the storage requirements for our CP-16 F-3DGS.

Qualitative results

For our methods, we used CP with a d of 16 in the case of our model, which is about 4-7 MB. For TensoRF, we visualized VM-48, which is about 16 MB. For 3DGS, we used the original 3DGS of 40-50 MB.

Visualization of F-3DGS and 3DGS. These visualize Gaussian points, ellipsoids and rendered images of three objects in Tanks&Temples dataset.

BibTeX



@inproceedings{10.1145/3664647.3681116,
author = {Sun, Xiangyu and Lee, Joo Chan and Rho, Daniel and Ko, Jong Hwan and Ali, Usman and Park, Eunbyung},
title = {F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting},
year = {2024},
isbn = {9798400706868},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3664647.3681116},
doi = {10.1145/3664647.3681116},
abstract = {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. Our project page is available at https://xiangyu1sun.github.io/Factorize-3DGS/.},
booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
pages = {7957–7965},
numpages = {9},
keywords = {3d reconstruction, compression, real-time rendering, tensor factorization},
location = {Melbourne VIC, Australia},
series = {MM '24}
}