FastGS: Training 3D Gaussian Splatting in 100 Seconds
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914182855655424 |
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| author | Ren, Shiwei Wen, Tianci Fang, Yongchun Lu, Biao |
| author_facet | Ren, Shiwei Wen, Tianci Fang, Yongchun Lu, Biao |
| contents | The dominant 3D Gaussian splatting (3DGS) acceleration methods fail to properly regulate the number of Gaussians during training, causing redundant computational time overhead. In this paper, we propose FastGS, a novel, simple, and general acceleration framework that fully considers the importance of each Gaussian based on multi-view consistency, efficiently solving the trade-off between training time and rendering quality. We innovatively design a densification and pruning strategy based on multi-view consistency, dispensing with the budgeting mechanism. Extensive experiments on Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets demonstrate that our method significantly outperforms the state-of-the-art methods in training speed, achieving a 3.32$\times$ training acceleration and comparable rendering quality compared with DashGaussian on the Mip-NeRF 360 dataset and a 15.45$\times$ acceleration compared with vanilla 3DGS on the Deep Blending dataset. We demonstrate that FastGS exhibits strong generality, delivering 2-7$\times$ training acceleration across various tasks, including dynamic scene reconstruction, surface reconstruction, sparse-view reconstruction, large-scale reconstruction, and simultaneous localization and mapping. The project page is available at https://fastgs.github.io/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_04283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FastGS: Training 3D Gaussian Splatting in 100 Seconds Ren, Shiwei Wen, Tianci Fang, Yongchun Lu, Biao Computer Vision and Pattern Recognition 68T40(Primary)68T45, 68U99 (Secondary) I.4.8; I.3.7 The dominant 3D Gaussian splatting (3DGS) acceleration methods fail to properly regulate the number of Gaussians during training, causing redundant computational time overhead. In this paper, we propose FastGS, a novel, simple, and general acceleration framework that fully considers the importance of each Gaussian based on multi-view consistency, efficiently solving the trade-off between training time and rendering quality. We innovatively design a densification and pruning strategy based on multi-view consistency, dispensing with the budgeting mechanism. Extensive experiments on Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets demonstrate that our method significantly outperforms the state-of-the-art methods in training speed, achieving a 3.32$\times$ training acceleration and comparable rendering quality compared with DashGaussian on the Mip-NeRF 360 dataset and a 15.45$\times$ acceleration compared with vanilla 3DGS on the Deep Blending dataset. We demonstrate that FastGS exhibits strong generality, delivering 2-7$\times$ training acceleration across various tasks, including dynamic scene reconstruction, surface reconstruction, sparse-view reconstruction, large-scale reconstruction, and simultaneous localization and mapping. The project page is available at https://fastgs.github.io/ |
| title | FastGS: Training 3D Gaussian Splatting in 100 Seconds |
| topic | Computer Vision and Pattern Recognition 68T40(Primary)68T45, 68U99 (Secondary) I.4.8; I.3.7 |
| url | https://arxiv.org/abs/2511.04283 |