Gang Li, Zhongliang Fu, Zhao Liu, Shengyuan Zhang
3D Gaussian splatting (3DGS) has significantly advanced the development of novel-view synthesis in both academic research and industrial applications due to its real-time rendering capability and excellent rendering quality. However, the visual quality of 3DGS is constrained when modeling complex scenes with specular highlights and anisotropic effects due to the limitations of low-order spherical harmonics in capturing high-frequency details. To address this issue, we propose a new method, Aniso-GS, which models the view-dependent appearance of each Gaussian by constructing an anisotropic appearance field. This introduces spatially adaptive spherical harmonic features that map low-order spherical harmonics into higher dimensions, significantly enhancing the model’s ability to capture high-frequency details. Additionally, we introduce a multi-view information smoothing strategy that improves generalization to novel viewpoints by smoothing model parameters across different training views. Furthermore, our adaptive densification strategy that suppresses Gaussian growth in low-frequency observation regions while promoting refinement in high-frequency regions, thus improving visual quality without increasing the total number of Gaussians. Quantitative and qualitative results demonstrate that our method achieves state-of-the-art visual quality on five public benchmark datasets. On the challenging Anisotropic Synthetic dataset, the PSNR improves by 5.86 dB compared to vanilla 3DGS, which clearly demonstrates the superior performance of our method in modeling specular highlights and anisotropic effects.