Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle Article Swipe
Youtian Lin
,
Zuozhuo Dai
,
Siyu Zhu
,
Yao Yao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.03431
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.03431
We introduce Gaussian-Flow, a novel point-based approach for fast dynamic scene reconstruction and real-time rendering from both multi-view and monocular videos. In contrast to the prevalent NeRF-based approaches hampered by slow training and rendering speeds, our approach harnesses recent advancements in point-based 3D Gaussian Splatting (3DGS). Specifically, a novel Dual-Domain Deformation Model (DDDM) is proposed to explicitly model attribute deformations of each Gaussian point, where the time-dependent residual of each attribute is captured by a polynomial fitting in the time domain, and a Fourier series fitting in the frequency domain. The proposed DDDM is capable of modeling complex scene deformations across long video footage, eliminating the need for training separate 3DGS for each frame or introducing an additional implicit neural field to model 3D dynamics. Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction. Our proposed approach showcases a substantial efficiency improvement, achieving a $5\times$ faster training speed compared to the per-frame 3DGS modeling. In addition, quantitative results demonstrate that the proposed Gaussian-Flow significantly outperforms previous leading methods in novel view rendering quality. Project page: https://nju-3dv.github.io/projects/Gaussian-Flow
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.03431
- https://arxiv.org/pdf/2312.03431
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389471228
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389471228Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.03431Digital Object Identifier
- Title
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Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian ParticleWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-06Full publication date if available
- Authors
-
Youtian Lin, Zuozhuo Dai, Siyu Zhu, Yao YaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.03431Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.03431Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.03431Direct OA link when available
- Concepts
-
Rendering (computer graphics), Computer science, Gaussian, Artificial intelligence, Computer vision, Discretization, Algorithm, Gaussian network model, Computer graphics (images), Mathematics, Mathematical analysis, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.reconstruction. | 154 |
| abstract_inverted_index.https://nju-3dv.github.io/projects/Gaussian-Flow | 196 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |