DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2507.15690v1
• Date: Mon, 21 Jul 2025 14:56:46 GMT
• Title: DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting
• Authors: Hung Nguyen, Runfa Li, An Le, Truong Nguyen,
• Abstract summary: Sparse-view 3D Gaussian Splatting (3DGS) presents significant challenges in reconstructing high-quality novel views.<n>We propose DWTGS, a framework that rethinks frequency regularization by leveraging wavelet-space losses that provide additional spatial supervision.
• Score: 5.026688852582894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sparse-view 3D Gaussian Splatting (3DGS) presents significant challenges in reconstructing high-quality novel views, as it often overfits to the widely-varying high-frequency (HF) details of the sparse training views. While frequency regularization can be a promising approach, its typical reliance on Fourier transforms causes difficult parameter tuning and biases towards detrimental HF learning. We propose DWTGS, a framework that rethinks frequency regularization by leveraging wavelet-space losses that provide additional spatial supervision. Specifically, we supervise only the low-frequency (LF) LL subbands at multiple DWT levels, while enforcing sparsity on the HF HH subband in a self-supervised manner. Experiments across benchmarks show that DWTGS consistently outperforms Fourier-based counterparts, as this LF-centric strategy improves generalization and reduces HF hallucinations.

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