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.

翻译：三维高斯泼溅在新视角实时合成任务中取得了显著成效。然而，在高斯密集化过程中，高频方差区域常被少量大尺寸高斯体覆盖，导致过拟合重建问题，进而引发渲染图像的模糊和伪影。为此，我们提出一种渐进式频率正则化（FreGS）技术，从频率空间解决该过拟合问题。具体而言，FreGS利用傅里叶空间中通过低通/高通滤波器可便捷提取的低频到高频分量，实现由粗到精的高斯密集化。通过最小化渲染图像频谱与真实图像频谱之间的差异，该方法实现了高质量的高斯密集化，并有效缓解了高斯泼溅的过拟合现象。在Mip-NeRF360、Tanks-and-Temples和Deep Blending等多个广泛采用的基准测试上，实验表明FreGS在新视角合成任务中表现优异，持续超越现有最优方法。