Low-Frequency First: Eliminating Floating Artifacts in 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2508.02493v2
• Date: Tue, 05 Aug 2025 15:28:05 GMT
• Title: Low-Frequency First: Eliminating Floating Artifacts in 3D Gaussian Splatting
• Authors: Jianchao Wang, Peng Zhou, Cen Li, Rong Quan, Jie Qin,
• Abstract summary: 3D Gaussian Splatting (3DGS) is a powerful representation for 3D reconstruction.<n>3DGS often produces floating artifacts, which are erroneous structures detached from the actual geometry.<n>We propose EFA-GS, which selectively expands under-optimized Gaussians to prioritize accurate low-frequency learning.
• Score: 22.626200397052862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) is a powerful and computationally efficient representation for 3D reconstruction. Despite its strengths, 3DGS often produces floating artifacts, which are erroneous structures detached from the actual geometry and significantly degrade visual fidelity. The underlying mechanisms causing these artifacts, particularly in low-quality initialization scenarios, have not been fully explored. In this paper, we investigate the origins of floating artifacts from a frequency-domain perspective and identify under-optimized Gaussians as the primary source. Based on our analysis, we propose \textit{Eliminating-Floating-Artifacts} Gaussian Splatting (EFA-GS), which selectively expands under-optimized Gaussians to prioritize accurate low-frequency learning. Additionally, we introduce complementary depth-based and scale-based strategies to dynamically refine Gaussian expansion, effectively mitigating detail erosion. Extensive experiments on both synthetic and real-world datasets demonstrate that EFA-GS substantially reduces floating artifacts while preserving high-frequency details, achieving an improvement of 1.68 dB in PSNR over baseline method on our RWLQ dataset. Furthermore, we validate the effectiveness of our approach in downstream 3D editing tasks. We provide our implementation in https://jcwang-gh.github.io/EFA-GS.

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