3D Gaussian Splatting (3DGS) has been explored for surface reconstruction, however unstructured and discontinuous Gaussian point clouds lead to uneven surface reconstruction accuracy as well as frequent loss of Novel View Synthesis (NVS) quality. To address this problem, we propose a scale-aligned monocular depth-guided 3DGS, a promising novel framework that combines geometric prior regularization and consistency supervision to achieve high-quality rendering and surface reconstruction. Specifically, monocular depth, estimated by some recently proposed monocular depth estimation models, contain implicitly abundant valuable geometric cues, but scale ambiguity limits its application. Therefore we first propose a $K$K-Nearest Neighbor (KNN)-based depth alignment framework that utilizes the full-domain gradient at monocular depth map to align to the sparse point cloud obtained during the Structure from Motion (SfM), which is employed for regularization to enhance geometric representation. Then a pseudo-mesh-based multi-view consistency module is introduced to fine-tune and guide the model to recover the accurate surface. Finally, a pixel-level isotropic gradient aware method guides the appropriate growth of the Gaussians to further improve the surface and rendering quality. Experiments on dozens of indoor, outdoor, and object-centered/non-object-centered datasets demonstrate that our method achieves accurate surface reconstruction with excellent NVS performance.

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.