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

arXiv: https://arxiv.org/abs/2507.15690 · HTML: https://mlsanity.com/paper/2507.15690/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 48 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 30 of 38)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization](https://mlsanity.com/paper/2303.07418/) (2023)
- [DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization](https://mlsanity.com/paper/2403.06912/) (2024)
- [Depth-Regularized Optimization for 3D Gaussian Splatting in Few-Shot Images](https://mlsanity.com/paper/2311.13398/) (2023)
- [SparseGS: Sparse View Synthesis using 3D Gaussian Splatting](https://mlsanity.com/paper/2312.00206/) (2023)
- [CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization](https://mlsanity.com/paper/2405.12110/) (2024)
- [Wavelet Diffusion Models are fast and scalable Image Generators](https://mlsanity.com/paper/2211.16152/) (2022)
- [3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors](https://mlsanity.com/paper/2410.16266/) (2024)
- [FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization](https://mlsanity.com/paper/2403.06908/) (2024)
- [Wavelet Convolutions for Large Receptive Fields](https://mlsanity.com/paper/2407.05848/) (2024)
- [DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting](https://mlsanity.com/paper/2504.00773/) (2025)
- [Masked Wavelet Representation for Compact Neural Radiance Fields](https://mlsanity.com/paper/2212.09069/) (2022)
- [WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model](https://mlsanity.com/paper/2411.17459/) (2024)
- [Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation](https://mlsanity.com/paper/2203.06321/) (2022)
- [Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction](https://mlsanity.com/paper/2305.15171/) (2023)
- [WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification](https://mlsanity.com/paper/2107.13335/) (2021)
- [WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields](https://mlsanity.com/paper/2308.04826/) (2023)
- [FewViewGS: Gaussian Splatting with Few View Matching and Multi-stage Training](https://mlsanity.com/paper/2411.02229/) (2024)
- [Structure Consistent Gaussian Splatting with Matching Prior for Few-shot Novel View Synthesis](https://mlsanity.com/paper/2411.03637/) (2024)
- [RI3D: Few-Shot Gaussian Splatting With Repair and Inpainting Diffusion Priors](https://mlsanity.com/paper/2503.10860/) (2025)
- [Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution](https://mlsanity.com/paper/2404.11273/) (2024)
- [Few-shot Novel View Synthesis using Depth Aware 3D Gaussian Splatting](https://mlsanity.com/paper/2410.11080/) (2024)
- [Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images](https://mlsanity.com/paper/2501.14231/) (2025)
- [DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes](https://mlsanity.com/paper/2503.11979/) (2025)
- [TriNeRFLet: A Wavelet Based Triplane NeRF Representation](https://mlsanity.com/paper/2401.06191/) (2024)
- [How to Use Diffusion Priors under Sparse Views?](https://mlsanity.com/paper/2412.02225/) (2024)

## Built on by (most cited first, 6 of 6)

- [WaveletGaussian: Wavelet-domain Diffusion for Sparse-view 3D Gaussian Object Reconstruction](https://mlsanity.com/paper/2509.19073/) (2025)
- [Learnable Multi-level Discrete Wavelet Transforms for 3D Gaussian Splatting Frequency Modulation](https://mlsanity.com/paper/2602.14199/) (2026)
- [LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction](https://mlsanity.com/paper/2601.17185/) (2026)
- [PairDropGS: Paired Dropout-Induced Consistency Regularization for Sparse-View Gaussian Splatting](https://mlsanity.com/paper/2605.12072/) (2026)
- [Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots](https://mlsanity.com/paper/2607.26567/) (2026)
- [SPLATIFY: Reproduce, Discover, Innovate! From Papers and Ideas to Trainable 3DGS Code](https://mlsanity.com/paper/2610.09116/) (2026)

## Abstract (arXiv)

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.