PSRGS:Progressive Spectral Residual of 3D Gaussian for High-Frequency Recovery

• URL: http://arxiv.org/abs/2503.00848v1
• Date: Sun, 02 Mar 2025 10:52:46 GMT
• Title: PSRGS:Progressive Spectral Residual of 3D Gaussian for High-Frequency Recovery
• Authors: BoCheng Li, WenJuan Zhang, Bing Zhang, YiLing Yao, YaNing Wang,
• Abstract summary: 3D Gaussian Splatting (3D GS) achieves impressive results in novel view synthesis for small, single-object scenes.<n>However, when applied to large-scale remote sensing scenes, 3D GS faces challenges.<n>We propose PSRGS, a progressive optimization scheme based on spectral residual maps.
• Score: 3.310033172069517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3D GS) achieves impressive results in novel view synthesis for small, single-object scenes through Gaussian ellipsoid initialization and adaptive density control. However, when applied to large-scale remote sensing scenes, 3D GS faces challenges: the point clouds generated by Structure-from-Motion (SfM) are often sparse, and the inherent smoothing behavior of 3D GS leads to over-reconstruction in high-frequency regions, where have detailed textures and color variations. This results in the generation of large, opaque Gaussian ellipsoids that cause gradient artifacts. Moreover, the simultaneous optimization of both geometry and texture may lead to densification of Gaussian ellipsoids at incorrect geometric locations, resulting in artifacts in other views. To address these issues, we propose PSRGS, a progressive optimization scheme based on spectral residual maps. Specifically, we create a spectral residual significance map to separate low-frequency and high-frequency regions. In the low-frequency region, we apply depth-aware and depth-smooth losses to initialize the scene geometry with low threshold. For the high-frequency region, we use gradient features with higher threshold to split and clone ellipsoids, refining the scene. The sampling rate is determined by feature responses and gradient loss. Finally, we introduce a pre-trained network that jointly computes perceptual loss from multiple views, ensuring accurate restoration of high-frequency details in both Gaussian ellipsoids geometry and color. We conduct experiments on multiple datasets to assess the effectiveness of our method, which demonstrates competitive rendering quality, especially in recovering texture details in high-frequency regions.

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