DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908580179869696 |
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| author | Nguyen, Hung Li, Runfa Le, An Nguyen, Truong |
| author_facet | Nguyen, Hung Li, Runfa Le, An Nguyen, Truong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting Nguyen, Hung Li, Runfa Le, An Nguyen, Truong Computer Vision and Pattern Recognition Image and Video Processing Signal Processing 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. |
| title | DWTGS: Rethinking Frequency Regularization for Sparse-view 3D Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2507.15690 |