  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.
