SGGS: セマンティック誘導型3Dガウシアン スプラッティングと適応型レンダリング

3D Gaussian Splatting (3DGS) has shown great promise in a variety of applications due to its exceptional real-time rendering quality and explicit representation, leading to numerous improvements across various fields. However, existing methods lack consideration of main objects and important structural information in their overall optimization strategies. This results in blurring of main objects in adaptive rendering and the loss of high-frequency details on targets that are insufficiently captured. In this work, we introduce a semantic-guided 3DGS method with adaptive rendering, which optimizes important structures through the guidance of boundary Gaussians, while leveraging semantic features to enhance the rendering of main objects in adaptive rendering. Experiments show that the proposed semantic-guided method can enhance important structures and high-frequency information in corner regions without significantly increasing the total number of Gaussians. This method also improves the separability between objects. At the same time, a semantic-guided Level-of-Detail (LoD) rendering approach enables the rapid display of main targets and the rendering of a complete scene. The semantic-guided methodology we have presented exhibits compatibility with a range of existing techniques. The code, more experimental results, and online demo will be available at https://zhouannan.github.io/SGGS/.