Frequency-Adaptive Sharpness Regularization for Improving 3D Gaussian Splatting Generalization
Despite 3D Gaussian Splatting (3DGS) excelling in most configurations, it lacks generalization across novel viewpoints in a few-shot scenario because it overfits to the sparse observations. We revisit 3DGS optimization from a machine learning perspective, framing novel view synthesis as a generalization problem to unseen viewpoints-an underexplored direction. We propose Frequency-Adaptive Sharpness Regularization (FASR), which reformulates the 3DGS training objective, thereby guiding 3DGS to converge toward a better generalization solution. Although Sharpness-Aware Minimization (SAM) similarly reduces the sharpness of the loss landscape to improve generalization of classification models, directly employing it to 3DGS is suboptimal due to the discrepancy between the tasks. Specifically, it hinders reconstructing high-frequency details due to excessive regularization, while reducing its strength leads to under-penalizing sharpness. To address this, we reflect the local frequency of images to set the regularization weight and the neighborhood radius when estimating the local sharpness. It prevents floater artifacts in novel viewpoints and reconstructs fine details that SAM tends to oversmooth. Across datasets with various configurations, our method consistently improves a wide range of baselines. Code will be available at https://bbangsik13.github.io/FASR.
692680db243b2216fb75cab2
Youngsik Yun
claimed_verified
2025-11-26T09:27:27.289Z
68e34e1f349df589247872e4
/avatars/884f38e7b4370d0661aab7a93dd1cd5d.svg
Youngsik Yun
bbangsik13
user
bbangsik13
692680db243b2216fb75cab3
Dongjun Gu
692680db243b2216fb75cab4
Youngjung Uh
Frequency-Adaptive Sharpness Regularization for Improving 3D Gaussian Splatting Generalization
Despite 3D Gaussian Splatting (3DGS) excelling in most configurations, it lacks generalization across novel viewpoints in a few-shot scenario because it overfits to the sparse observations. We revisit 3DGS optimization from a machine learning perspective, framing novel view synthesis as a generalization problem to unseen viewpoints-an underexplored direction. We propose Frequency-Adaptive Sharpness Regularization (FASR), which reformulates the 3DGS training objective, thereby guiding 3DGS to converge toward a better generalization solution. Although Sharpness-Aware Minimization (SAM) similarly reduces the sharpness of the loss landscape to improve generalization of classification models, directly employing it to 3DGS is suboptimal due to the discrepancy between the tasks. Specifically, it hinders reconstructing high-frequency details due to excessive regularization, while reducing its strength leads to under-penalizing sharpness. To address this, we reflect the local frequency of images to set the regularization weight and the neighborhood radius when estimating the local sharpness. It prevents floater artifacts in novel viewpoints and reconstructs fine details that SAM tends to oversmooth. Across datasets with various configurations, our method consistently improves a wide range of baselines. Code will be available at https://bbangsik13.github.io/FASR.
Frequency-Adaptive Sharpness Regularization (FASR) enhances 3D Gaussian Splatting's generalization to novel viewpoints by adaptively adjusting regularization based on local image frequency.
3D Gaussian Splatting 3DGS Frequency-Adaptive Sharpness Regularization FASR Sharpness-Aware Minimization SAM generalization novel view synthesis sharpness regularization high-frequency details floater artifacts