F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting
(2405.17083)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.
Overview
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The paper introduces F-3DGS, a novel method to mitigate the storage and computational challenges in 3D Gaussian Splatting by utilizing factorization techniques.
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F-3DGS employs canonical polyadic (CP) and vector-matrix (VM) decompositions to significantly reduce the storage requirements of 3D Gaussian coordinates and attributes while maintaining high rendering quality.
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Experimental results show that F-3DGS achieves competitive performance on both synthetic and real-world datasets with significantly reduced model sizes, making it ideal for resource-limited environments such as AR/VR and gaming.
Factorized Coordinates and Representations for 3D Gaussian Splatting (F-3DGS)
The paper "F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting" by Xiangyu Sun et al. introduces a significant advancement in the domain of neural rendering and 3D scene representation. The authors present a novel method, termed F-3DGS, that leverages factorization techniques to address the computational and storage constraints inherent in the 3D Gaussian Splatting (3DGS) method.
Background and Motivation
Neural Radiance Fields (NeRF) have been widely recognized for their efficacy in high-quality 3D scene representation and novel view synthesis. However, the computational intensity and storage demands of NeRF impede its application in resource-constrained and real-time environments. On the other hand, 3DGS offers rapid rendering speeds while maintaining high image quality by circumventing the need for dense sampling inherent in NeRF. Yet, the storage requirement for high-quality scenes in 3DGS remains substantial due to the large number of Gaussians used.
Proposed Method: F-3DGS
To mitigate the storage overhead, the authors propose Factorized 3D Gaussian Splatting (F-3DGS), which aims to reduce the storage complexity while preserving the rendered image quality. Inspired by classical matrix and tensor factorization techniques, F-3DGS employs two primary methods for factorization: canonical polyadic (CP) and vector-matrix (VM) decompositions.
Factorized Coordinates
The paper introduces factorized coordinates as a means to efficiently represent and approximate dense clusters of Gaussians. By adopting CP decomposition, the coordinates of 3D Gaussians are parameterized using smaller sets of 1D or 2D coordinates, significantly reducing the number of parameters required. For example, factorized coordinates aligned along axes can represent up to one billion points using only a few thousand numbers. This reduction is achieved without compromising the flexibility of the positions, which is critical for high-quality rendering.
Factorized Representations
In addition to coordinate factorization, the authors also factorize associated attributes of Gaussians such as color, scale, rotation, and opacity. These attributes are decomposed using both CP and VM techniques, allowing the representation of 3D Gaussians to be compressed further. The CP approach factorizes attributes along each axis, while the VM approach uses plane-based decompositions to increase positional flexibility.
Initialization and Masking
The initialization scheme plays a pivotal role in achieving high rendering quality. The authors propose a heuristic method to initialize the positions of Gaussians based on a pre-trained 3DGS model, ensuring a close approximation of the scene's actual geometry.
Moreover, F-3DGS incorporates a masking mechanism to prune redundant Gaussians that do not contribute to the rendering quality. Employing binary masks generated and optimized during training, this method effectively reduces the computational burden by eliminating irrelevant Gaussians, thus accelerating both the training and rendering processes.
Experimental Results
Extensive experiments demonstrate that F-3DGS can significantly reduce storage requirements while maintaining comparable rendering quality. On the synthetic-NeRF dataset, the CP-based F-3DGS achieves a 32.42 PSNR with only 6.06 MB, whereas the VM-based F-3DGS reaches 33.24 PSNR with 28.75 MB. This performance is on par with or superior to state-of-the-art methods like TensoRF and Strivec, but with a fraction of the storage cost. For real-world datasets such as Tanks{content}Temples and Mip-NeRF 360, F-3DGS exhibits similar competitive performance, achieving substantial reductions in model size and maintaining high visual quality.
Implications and Future Directions
The introduction of F-3DGS has profound implications for the fields of neural rendering and 3D reconstruction. The factorization techniques allow for efficient storage and real-time rendering, making high-quality 3D scene representation feasible in resource-limited environments. This methodology could be particularly beneficial for applications in AR/VR, gaming, and online 3D content delivery where storage and computational efficiency are paramount.
Future research could explore extending the factorization approach to more complex and unbounded scenes. Additionally, integrating deep learning models with F-3DGS could further enhance the fidelity and scalability of 3D scene representations. There is also potential in optimizing the rendering pipeline to fully exploit the compressed representations, thereby pushing the boundaries of real-time neural rendering.
In conclusion, the paper by Xiangyu Sun et al. presents a compelling and practical advancement in 3D scene representation, offering a scalable solution to the storage and computational challenges of existing methods. The proposed F-3DGS method sets a new benchmark in terms of efficiency and quality, paving the way for broader application and further innovation in the domain of neural rendering.
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