Instance Segmentation on 3D City Meshes for Building Extraction
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
Digital twins are becoming increasingly popular in society for performing simulations. However, to conduct simulations, it is necessary to extract information about the objects composing an urban environment. Recently, a new semantic segmentation model applied to textured meshes, named PicassoNet-II, has been developed. The architecture of this model will be modified to perform segmentation of building instances rather than semantic segmentation. Additionally, a contextual analysis based on Markov fields is integrated into the algorithm to perform a contextual analysis of the features following segmentation. To train a 3D city segmentation model that can be generalized to any dataset, a large amount of annotated data is required. The model is trained using real data from Quebec City, Canada, as well as simulated data from different platforms such as Unreal Engine and Evermotion. Experimental results on semantic segmentation demonstrate that both simulated data and a Markov based analysis improves segmentation results overall.
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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