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arXiv · 2501.00625

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

Kyle Gao·Liangzhi Li·Hongjie He·Dening Lu·Linlin Xu·Jonathan Li
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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.

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Kyle Gao, Liangzhi Li, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li. 2024-12-31. Gaussian Building Mesh (GBM): Extract a Building's 3D Mesh with Google Earth and Gaussian Splatting. https://doi.org/10.1016/j.rsase.2025.101807

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