Abstract
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.
Abstract (translated)
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）允许在多视角二维图像中以几何一致性的方式对感兴趣的物体进行分割。用户可以使用基于文本或点击的提示来分割感兴趣的对象，而无需依赖标注的训练数据集。高斯点阵技术则可以根据二维图像学习场景的三维几何和辐射表示。通过结合Google Earth Studio、SAM2+GroundingDINO、二维高斯点阵以及我们在掩码细化方面的改进（基于形态操作和轮廓简化），我们创建了一个管道，可以仅凭建筑物的名字、地址或地理坐标提取其3D网格模型。
URL
https://arxiv.org/abs/2501.00625
