Over the last few decades, image-based building surface reconstruction has garnered substantial research interest and has been applied across various fields, such as heritage preservation, architectural planning, etc. Compared to the traditional photogrammetric and NeRF-based solutions, recently, Gaussian fields-based methods have exhibited significant potential in generating surface meshes due to their time-efficient training and detailed 3D information preservation. However, most gaussian fields-based methods are trained with all image pixels, encompassing building and nonbuilding areas, which results in a significant noise for building meshes and degeneration in time efficiency. This paper proposes a novel framework, Masked Gaussian Fields (MGFs), designed to generate accurate surface reconstruction for building in a time-efficient way. The framework first applies EfficientSAM and COLMAP to generate multi-level masks of building and the corresponding masked point clouds. Subsequently, the masked gaussian fields are trained by integrating two innovative losses: a multi-level perceptual masked loss focused on constructing building regions and a boundary loss aimed at enhancing the details of the boundaries between different masks. Finally, we improve the tetrahedral surface mesh extraction method based on the masked gaussian spheres. Comprehensive experiments on UAV images demonstrate that, compared to the traditional method and several NeRF-based and Gaussian-based SOTA solutions, our approach significantly improves both the accuracy and efficiency of building surface reconstruction. Notably, as a byproduct, there is an additional gain in the novel view synthesis of building.
在过去几十年中，基于图像的建筑表面重建吸引了大量研究兴趣，并广泛应用于遗产保护、建筑规划等领域。与传统的摄影测量和 NeRF 基于的解决方案相比，最近基于 Gaussian 场的方法在生成表面网格方面表现出了显著的潜力，因为它们在训练时间上更高效且能够保留详细的 3D 信息。然而，大多数基于 Gaussian 场的方法在训练时会使用所有图像像素，包括建筑物和非建筑物区域，这会导致建筑网格的显著噪声并降低时间效率。为了解决这些问题，本文提出了一个新颖的框架——遮罩 Gaussian 场（MGFs），旨在以时间高效的方式生成准确的建筑表面重建。该框架首先应用 EfficientSAM 和 COLMAP 生成建筑物的多层次遮罩及相应的遮罩点云。随后，通过整合两个创新损失函数来训练遮罩 Gaussian 场：一个关注建筑区域构建的多层次感知遮罩损失，以及一个旨在增强不同遮罩之间边界细节的边界损失。最后，我们改进了基于遮罩 Gaussian 球体的四面体表面网格提取方法。通过对无人机图像的全面实验表明，与传统方法及若干 NeRF 基于和 Gaussian 基于的最新解决方案相比，我们的方法显著提高了建筑表面重建的准确性和效率。值得注意的是，作为副产品，我们还在建筑的新视角合成中获得了额外的提升。