The application prospects of photorealistic 3D reconstruction are broad in smart cities, cultural heritage preservation, and related domains. However, existing methods face persistent challenges in balancing reconstruction accuracy, computational efficiency, and robustness, particularly in complex scenes characterized by reflective surfaces, vegetation, sparse viewpoints, or large-scale structures. In this study, an enhanced 3D Gaussian Splatting (3DGS) framework that integrates three key innovations is proposed: (i) a depth-aware regularization module that leverages metric depth priors from the pre-trained Depth-Anything V2 model, enabling geometrically informed optimization through a dynamically weighted hybrid loss; (ii) a gradient-driven adaptive densification mechanism that triggers Gaussian adjustments based on local gradient saliency, reducing redundant computation; and (iii) a neighborhood density-based floating artifact detection method that filters outliers using spatial distribution and opacity thresholds. Extensive evaluations are conducted across four diverse datasets—ranging from architectures, urban scenes, natural landscapes with water bodies, and long-range linear infrastructures. Our method achieves state-of-the-art performance in both reconstruction quality and efficiency, attaining a PSNR of 34.15 dB and SSIM of 0.9382 on medium-sized scenes, with real-time rendering speeds exceeding 170 FPS at a resolution of 1600 × 900. It demonstrates superior generalization on challenging materials such as water and foliage, while exhibiting reduced overfitting compared to baseline approaches. Ablation studies confirm the critical contributions of depth regularization and gradient-sensitive adaptation, with the latter improving training efficiency by 38% over depth supervision alone. Furthermore, we analyze the impact of input resolution and depth model selection, revealing non-trivial trade-offs between quantitative metrics and visual fidelity. While aggressive downsampling inflates PSNR and SSIM, it leads to loss of high-frequency detail; we identify 1/4–1/2 resolution scaling as an optimal balance for practical deployment. Among depth models, Vitb achieves the best reconstruction stability. Despite these advances, memory consumption remains a challenge in large-scale scenarios. Future work will focus on lightweight model design, efficient point cloud preprocessing, and dynamic memory management to enhance scalability for industrial applications.

The application prospects of multi-temporal 3D models are broad. It is difficult to ensure that multi-temporal 3D models have a consistent spatial reference. In this study, a method for automatic alignment of multi-temporal 3D models based on phase congruency (PC) matching is proposed. Firstly, the texture image of the multi-temporal 3D model is obtained, and the key points are extracted from the texture image. Secondly, the affine model between the plane of the key point and its corresponding tile triangle is established, and the 2D coordinates of the key point are mapped to 3D spatial coordinates. Thirdly, multi-temporal 3D model matching is completed based on PC to obtain a large number of evenly distributed corresponding points. Finally, the parameters of the 3D transformation model are estimated based on the multi-temporal corresponding points, and the vertex update of the 3D model is completed. The first experiment demonstrates that the method proposed in this study performs remarkably well in improving the positioning accuracy of feature point coordinates, effectively reducing the mean error of the systematic error to below 0.001 m. The second experiment further reveals the significant impact of different 3D transformation models. The experimental results show that the coordinates obtained based on position and orientation system (POS) data have significant positioning errors, while the method proposed in this study can reduce the coordinate errors between the two-period models. Due to the fact that this method does not require obtaining ground control points (GCPs) and does not require manual measurement for 3D geometric registration, its application to multi-temporal 3D models can ensure high-precision spatial referencing for multi-temporal 3D models, streamlining processes to reduce resource intensity and enhancing economic efficiency.