Gaussian Building Mesh (GBM): Extract a building’s 3D mesh with Google Earth and Gaussian Splatting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid convergence of computer vision and digital technologies is redefining how buildings are captured, modeled, and managed. In computer vision, recently released open-source pre-trained foundational image segmentation and object detection models allow for geometrically consistent segmentation of objects of interest in multi-view 2D images. Text-based or click-based prompts can be used to segment objects of interest without requiring labeled training datasets, allowing for both user-prompted and automated segmentation. Simultaneously, Gaussian Splatting allows for learning a 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. Our pipeline offers a fast and user-accessible framework for rapid 3D modeling of built environments and structures, enabling downstream applications. • Novel 3D mesh extraction pipeline based on text or click-based user input. • Extract a building 3D mesh from its name, address, or geocoding information. • Uses Google Earth data and does not require any on-site data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it