3D point clouds simplification based on geometric primitives and graph-structured optimization
Why this work is in the frame
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Bibliographic record
Abstract
We present a new method for the simplification of 3D point clouds for digital twin city models. Such data usually contains a large amount of redundant information, noise and outliers. This implies that most of subsequent processing tasks are costly both in terms of processing times and hardware infrastructure. The core idea of this paper is that most of the objects present in such scenes can be approximated as a combination of simple geometric primitives. This approximation can, in turn, be simplified by keeping only points and edges that effectively describe the shape of the input scene. Our main contribution is a formulation of the approximation of 3D point clouds with simple geometric primitives as a global optimization problem. We then introduce a new algorithm to efficiently solve this problem with a graph-cut-based approach. We measure the performances of our approach against state-of-the-art methods by comparing the geometric quality of the approximations with the amount of information needed to represent the simplified models. The evaluation of our approach is done on Mobile Laser Scanning (MLS) acquisitions in urban areas. Test areas include single objects and street portions to show the adaptability of our method to various geometries. We show improvements both in terms of geometrical error and final model size.
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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 | 0.001 |
| 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) | 0.016 | 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