AH-GS: Augmented 3D Gaussian Splatting for High-Frequency Detail Representation

• URL: http://arxiv.org/abs/2503.22324v1
• Date: Fri, 28 Mar 2025 10:57:33 GMT
• Title: AH-GS: Augmented 3D Gaussian Splatting for High-Frequency Detail Representation
• Authors: Chenyang Xu, XingGuo Deng, Rui Zhong,
• Abstract summary: AH-GS enables 3D Gaussians in structurally complex regions to obtain higher-frequency encodings.<n>Our method exceeds the rendering quality of Scaffold-GS in just 15K iterations.
• Score: 5.793751303065176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 3D Gaussian Splatting (3D-GS) is a novel method for scene representation and view synthesis. Although Scaffold-GS achieves higher quality real-time rendering compared to the original 3D-GS, its fine-grained rendering of the scene is extremely dependent on adequate viewing angles. The spectral bias of neural network learning results in Scaffold-GS's poor ability to perceive and learn high-frequency information in the scene. In this work, we propose enhancing the manifold complexity of input features and using network-based feature map loss to improve the image reconstruction quality of 3D-GS models. We introduce AH-GS, which enables 3D Gaussians in structurally complex regions to obtain higher-frequency encodings, allowing the model to more effectively learn the high-frequency information of the scene. Additionally, we incorporate high-frequency reinforce loss to further enhance the model's ability to capture detailed frequency information. Our result demonstrates that our model significantly improves rendering fidelity, and in specific scenarios (e.g., MipNeRf360-garden), our method exceeds the rendering quality of Scaffold-GS in just 15K iterations.

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