标题： 高斯建筑网格 (GBM)：利用谷歌地球和高斯散射提取建筑物的三维网格 显示英文标题

标题： Gaussian Building Mesh (GBM): Extract a Building's 3D Mesh with Google Earth and Gaussian Splatting

摘要： 最近发布的开源预训练基础图像分割和目标检测模型（SAM2+GroundingDINO）允许对多视图二维图像中感兴趣的物体进行几何一致的分割。 用户可以使用基于文本或基于点击的提示来分割感兴趣的物体，而无需标记的训练数据集。 高斯点绘法允许根据二维图像学习场景几何和辐射的三维表示。 结合Google Earth Studio、SAM2+GroundingDINO、二维高斯点绘法以及我们在基于形态学操作和轮廓简化方面的掩模细化改进，我们创建了一个管道，可以根据建筑物的名称、地址或地理坐标提取该建筑物的三维网格。 显示英文摘要

摘要： Recently released open-source pre-trained foundational image segmentation and object detection models (SAM2+GroundingDINO) allow for geometrically consistent segmentation of objects of interest in multi-view 2D images. Users can use text-based or click-based prompts to segment objects of interest without requiring labeled training datasets. Gaussian Splatting allows for the learning of the 3D representation of a scene's geometry and radiance based on 2D images. Combining Google Earth Studio, SAM2+GroundingDINO, 2D Gaussian Splatting, and our improvements in mask refinement based on morphological operations and contour simplification, we created a pipeline to extract the 3D mesh of any building based on its name, address, or geographic coordinates.