Abstract:

3D Gaussian Splatting (3DGS) technology has demonstrated exceptional performance in the field of novel view synthesis. However, when processing complex scenes with abundant details, its conventional densification strategy is highly susceptible to the over-reconstruction phenomenon. This leads to a small number of large-volume Gaussian primitives fitting extensive areas, resulting in noticeable blurriness in rendered images and a severe loss of high-frequency texture details. To overcome this issue, a novel Frequency Intensity Gaussian Splatting (FIGS) algorithm was proposed. First, starting from local image frequency-domain characteristics, a “Frequency Intensity” metric was defined to precisely quantify the richness of high-frequency details within local spatial regions, establishing corresponding frequency intensity maps. Second, a frequency-intensity regularization mechanism was designed. By incorporating the discrepancy between the frequency-intensity maps of the rendered images and the ground truth into the loss function, this mechanism drove the algorithm to adaptively allocate more Gaussian primitives to over-reconstructed regions densely populated with high-frequency details. Finally, a confidence-based adaptive Gaussian evolution strategy was introduced. Leveraging multi-view consistency priors to construct a geometric confidence factor, this strategy achieved robust Gaussian evolution through a joint mechanism of weighted densification and active pruning, thereby dynamically optimizing Gaussian allocation across the entire scene. Extensive qualitative and quantitative experiments conducted on multiple real-world benchmark datasets, including Mip-NeRF360 and Tanks & Temples, demonstrated that the proposed algorithm achieved highly competitive results in evaluation metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS), effectively reducing the proportion of over-reconstructed regions. The Frequency Intensity Gaussian Splatting algorithm could effectively alleviate blurriness during the reconstruction process and scientifically orchestrate the spatial distribution of Gaussian primitives. It exhibited outstanding performance in detail fidelity and overall visual quality for novel view synthesis, providing a robust new solution for high-precision 3D reconstruction of complex real-world scenes.

Key words: novel view synthesis, 3D Gaussian splatting, 3D reconstruction, over-reconstruction, frequency-domain analysis