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.
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）可以在多视角二维图像中实现几何一致的目标分割。用户可以使用基于文本或点击的提示，分割感兴趣的目标，而无需依赖标注的训练数据集。高斯喷射（Gaussian Splatting）则可以基于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、二维高斯喷射以及我们在基于形态学操作和轮廓简化的遮罩优化方面的改进，我们创建了一套流程，可以基于建筑的名称、地址或地理坐标提取其三维网格模型。