FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization
<|reference_start|>FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization: 3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis. However, it often suffers from over-reconstruction during Gaussian densification where high-variance image regions are covered by a few large Gaussians only, leading to blur and artifacts in the rendered images. We design a progressive frequency regularization (FreGS) technique to tackle the over-reconstruction issue within the frequency space. Specifically, FreGS performs coarse-to-fine Gaussian densification by exploiting low-to-high frequency components that can be easily extracted with low-pass and high-pass filters in the Fourier space. By minimizing the discrepancy between the frequency spectrum of the rendered image and the corresponding ground truth, it achieves high-quality Gaussian densification and alleviates the over-reconstruction of Gaussian splatting effectively. Experiments over multiple widely adopted benchmarks (e.g., Mip-NeRF360, Tanks-and-Temples and Deep Blending) show that FreGS achieves superior novel view synthesis and outperforms the state-of-the-art consistently.<|reference_end|>
@article{zhang2024fregs:,
    title={FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization},
    author={Jiahui Zhang and Fangneng Zhan and Muyu Xu and Shijian Lu and Eric
  Xing},
    journal={arXiv preprint arXiv:2403.06908},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06908},
    primaryClass={cs.CV}
}