毕业生知识库

3D 建筑物模型是智慧城市数据的重要表达载体，在无人驾驶、城市管理、风场模拟、建筑节能分析等领域具有重要应用价值。然而，当前建筑物场景的3D模型重建面临诸如训练数据不足、模型泛化能力差、点云噪声和数据缺失、以及3D建模精细度不足等问题。为解决这些难题，本文创新性地提出了一种联合多视角三维重建的建筑物细粒度语义建模框架。

首先，本文提出了一种结合二维图像和三维点云的语义分割方法。通过少量图像标注训练，获得基础的2D语义分割模型。利用多视角图像的光度一致性和3D点云的几何一致性约束，从多视角图像中融合得到鲁棒的3D点云语义结果。实验表明，本文的方法能够实现高质量的点云语义分割精度，特别是在困难的语义边缘区域。

此外，本文还提出了一种基于多视角图像的3D特征线生成及边界优化算法。该算法利用多视角图像中的边缘信息，在3D空间中基于点云平面进行融合和增强，从而获得高精度的3D特征线。在此基础上，将边界生成视为图论中的最小环路问题，并通过求解最小环路问题得到简洁且平滑的建筑面边界。实验证明，相较于其他方法，本文生成的模型具有更平滑的边缘和更轻量化的表示，同时避免了点云噪声对建模结果的不良影响。

最后，本文的边界生成算法可以将像素级别的细粒度语义分割结果转化为紧凑的几何多边形表示，无缝地融合到轻量化的3D建筑模型中。这显著提高了模型的语义精细度，拓宽了实际应用范围。实验结果表明，通过细粒度语义与轻量化模型的融合，可以得到更为丰富、精细且真实的3D建筑物模型。

综上所述，本文的方法在处理点云噪声、数据缺失以及生成更高精细度的建筑物模型方面表现出色，为实际应用提供了可靠基础，有望在智慧城市建设及其他领域发挥关键作用，为未来智慧城市的发展带来实质性的贡献。

3D building models play a crucial role in various applications of smart city construction, such as autonomous driving, wind field simulation, and energy efficiency analysis of buildings. However, current 3D building scene reconstruction faces challenges such as insufficient training data, poor model generalization capabilities, point cloud noise and missing data, as well as inadequate 3D modeling precision. To address these issues, this paper innovatively proposes a fine-grained semantic modeling framework for buildings based on joint multi-view 3D reconstruction.

Firstly, this paper presents a semantic segmentation method that combines 2D images and 3D point clouds. A basic 2D semantic segmentation model is obtained through training with a limited number of annotated images. Using the photometric consistency of multi-view images and the geometric consistency of 3D point cloud content, a robust 3D point cloud semantic result is achieved by merging information from multiple viewpoints. Experiments show that this approach achieves high-quality point cloud semantic segmentation accuracy, particularly in challenging semantic boundary regions.

Furthermore, this paper introduces a 3D feature line generation and boundary optimization algorithm based on multi-view images. By leveraging edge information from multi-view images, the algorithm fuses and enhances point cloud planes in 3D space, resulting in high-precision 3D feature lines. Based on these feature lines, boundary generation is treated as a minimum cycle problem in graph theory, and concise and smooth building boundaries are obtained by solving the problem. Experiments demonstrate that, compared to other methods, the models generated by this approach have smoother edges and lighter representations, while avoiding the adverse effects of point cloud noise on modeling results.

Finally, the boundary generation algorithm proposed in this paper can convert pixel-level fine-grained semantic segmentation results into compact geometric polygon representations, seamlessly integrating them into lightweight 3D building models. This significantly improves the semantic precision of the models and broadens their practical application scope. Experimental results indicate that the fusion of fine-grained semantics and lightweight models produces richer, more detailed, and realistic 3D building models.

In conclusion, the methods presented in this paper excel in handling point cloud noise, data missing, and generating higher precision building models, providing a solid foundation for practical applications. The innovative techniques proposed in this paper offer important groundwork for addressing the numerous challenges in generating 3D building models, and are expected to play a key role in smart city construction and other fields, making substantial contributions to the development of future smart cities.