DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912294503448576 |
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| author | Chen, Youyu Jiang, Junjun Jiang, Kui Tang, Xiao Li, Zhihao Liu, Xianming Nie, Yinyu |
| author_facet | Chen, Youyu Jiang, Junjun Jiang, Kui Tang, Xiao Li, Zhihao Liu, Xianming Nie, Yinyu |
| contents | 3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, dominate the time cost in primitive optimization. In this paper, we propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS that strips redundant complexity to accelerate 3DGS optimization. Specifically, we formulate 3DGS optimization as progressively fitting 3DGS to higher levels of frequency components in the training views, and propose a dynamic rendering resolution scheme that largely reduces the optimization complexity based on this formulation. Besides, we argue that a specific rendering resolution should cooperate with a proper primitive number for a better balance between computing redundancy and fitting quality, where we schedule the growth of the primitives to synchronize with the rendering resolution. Extensive experiments show that our method accelerates the optimization of various 3DGS backbones by 45.7% on average while preserving the rendering quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds Chen, Youyu Jiang, Junjun Jiang, Kui Tang, Xiao Li, Zhihao Liu, Xianming Nie, Yinyu Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, dominate the time cost in primitive optimization. In this paper, we propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS that strips redundant complexity to accelerate 3DGS optimization. Specifically, we formulate 3DGS optimization as progressively fitting 3DGS to higher levels of frequency components in the training views, and propose a dynamic rendering resolution scheme that largely reduces the optimization complexity based on this formulation. Besides, we argue that a specific rendering resolution should cooperate with a proper primitive number for a better balance between computing redundancy and fitting quality, where we schedule the growth of the primitives to synchronize with the rendering resolution. Extensive experiments show that our method accelerates the optimization of various 3DGS backbones by 45.7% on average while preserving the rendering quality. |
| title | DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.18402 |