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.
3D 高斯喷溅技术在实时新视角合成中取得了非常令人印象深刻的性能。然而，它在高斯密集化过程中经常遭受过度重建的问题，其中高方差图像区域只被少数几个大高斯覆盖，导致渲染图像中出现模糊和伪影。我们设计了一种渐进式频率正则化（FreGS）技术来解决频率空间内的过度重建问题。具体而言，FreGS通过利用低通和高通滤波器在傅里叶空间轻松提取的低频至高频成分，执行由粗到细的高斯密集化。通过最小化渲染图像的频率谱与相应真实图像之间的差异，它实现了高质量的高斯密集化，并有效缓解了高斯喷溅的过度重建问题。在多个广泛采用的基准测试（例如 Mip-NeRF360、Tanks-and-Temples 和 Deep Blending）上的实验表明，FreGS实现了卓越的新视角合成，并一致性地超越了最先进的技术。