Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

F-3DGS: Factorized Coordinates and Representations for 3D Gaussian Splatting (2405.17083v2)

Published 27 May 2024 in cs.CV

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Where and how: Mitigating confusion in neural radiance fields from sparse inputs. In Proceedings of the 31st ACM International Conference on Multimedia, MM 2023, Ottawa, ON, Canada, 29 October 2023- 3 November 2023, pages 2180–2188. ACM, 2023.
  2. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021.
  3. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5470–5479, 2022.
  4. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013.
  5. Hexplane: A fast representation for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 130–141, 2023.
  6. Segment anything in 3d with neRFs. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
  7. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16123–16133, 2022.
  8. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR], Stanford University — Princeton University — Toyota Technological Institute at Chicago, 2015.
  9. Tensorf: Tensorial radiance fields. In European Conference on Computer Vision, 2022.
  10. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 16569–16578, 2023.
  11. Plenoxels: Radiance fields without neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501–5510, 2022.
  12. K-planes: Explicit radiance fields in space, time, and appearance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12479–12488, 2023.
  13. Strivec: Sparse tri-vector radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 17569–17579, 2023.
  14. Eagles: Efficient accelerated 3d gaussians with lightweight encodings. arXiv preprint arXiv:2312.04564, 2023.
  15. Recolornerf: Layer decomposed radiance fields for efficient color editing of 3d scenes. In Proceedings of the 31st ACM International Conference on Multimedia, pages 8004–8015, 2023.
  16. Instruct-nerf2nerf: Editing 3d scenes with instructions. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 19740–19750, 2023.
  17. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics (ToG), 42(4):1–14, 2023.
  18. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics, 36(4), 2017.
  19. Point-based neural rendering with per-view optimization. In Computer Graphics Forum, pages 29–43, 2021.
  20. Pulsar: Efficient sphere-based neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1440–1449, 2021.
  21. Compact 3d gaussian representation for radiance field. arXiv preprint arXiv:2311.13681, 2023.
  22. Neural sparse voxel fields. Advances in Neural Information Processing Systems, pages 15651–15663, 2020.
  23. Instance neural radiance field. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 787–796, 2023.
  24. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. arXiv preprint arXiv:2312.00109, 2023.
  25. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision, page 405–421, 2020.
  26. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4), 2022.
  27. Compressed 3d gaussian splatting for accelerated novel view synthesis. arXiv preprint arXiv:2401.02436, 2023.
  28. Radsplat: Radiance field-informed gaussian splatting for robust real-time rendering with 900+ fps. arXiv.org, 2024.
  29. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 2019.
  30. Dreamfusion: Text-to-3d using 2d diffusion. In The Eleventh International Conference on Learning Representations, 2023.
  31. Octree-gs: Towards consistent real-time rendering with lod-structured 3d gaussians. arXiv preprint arXiv:2403.17898, 2024.
  32. Masked wavelet representation for compact neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20680–20690, 2023.
  33. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  34. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5459–5469, 2022.
  35. Clip-nerf: Text-and-image driven manipulation of neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3835–3844, 2022.
  36. NeRF-IBVS: Visual servo based on neRF for visual localization and navigation. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.
  37. Point-nerf: Point-based neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5438–5448, 2022.
  38. Freenerf: Improving few-shot neural rendering with free frequency regularization. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2023.
  39. Plenoctrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752–5761, 2021a.
  40. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578–4587, 2021b.
  41. Surface splatting. In Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, page 371–378, New York, NY, USA, 2001. Association for Computing Machinery.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Xiangyu Sun (16 papers)
  2. Joo Chan Lee (10 papers)
  3. Daniel Rho (13 papers)
  4. Jong Hwan Ko (30 papers)
  5. Usman Ali (33 papers)
  6. Eunbyung Park (42 papers)
Citations (3)
View on Semantic Scholar

Summary

  • The paper introduces F-3DGS, which factorizes both 3D Gaussian coordinates and attributes via CP and VM decompositions to lower storage requirements.
  • The method compresses dense 3D data by representing large clusters with minimal parameters while maintaining high rendering quality.
  • Experimental results show F-3DGS achieves competitive PSNR scores on synthetic and real datasets, substantially reducing model size for real-time applications.

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.