FastGS: Training 3D Gaussian Splatting in 100 Seconds

• URL: http://arxiv.org/abs/2511.04283v1
• Date: Thu, 06 Nov 2025 11:21:16 GMT
• Title: FastGS: Training 3D Gaussian Splatting in 100 Seconds
• Abstract summary: We propose FastGS, a novel, simple, and general acceleration framework.<n>It fully considers the importance of each Gaussian based on multi-view consistency.<n>It efficiently solves the trade-off between training time and rendering quality.
• Score: 15.1354395071878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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