Detecting Discontinuous and Occluded Boundaries from Point Clouds of Building Interiors
Bibliographic record
Abstract
Range scans of occupied building interiors will often generate a cumulative point cloud with disconnected regions due to varying data density and the presence of numerous partially occluded objects. Boundary detection methods based on surface normal vectors and curvature have difficulty in accurately representing occluded region boundaries because of the geometric uncertainty associated with the underlying polygonal surfaces used to determine the desired geometric parameters. An occluded boundary detection algorithm that works directly on point clouds without the need to reconstruct rough underlying surface models is presented in this paper. The algorithm uses a side-ratio constraint to identify the discontinuous boundary points which lie along successive scan lines. The basic principle is that the distance between the immediate neighboring points at the discontinuous boundary exhibits a large disparity when compared to the other points in a contiguous surface. The side ratio distance defines the spatial separation of the proceeding and succeeding data points on the local grid. The algorithm is also able to handle small density inconsistencies by continuously comparing the side-ratios of the nearest points within a preset window. Spurious point data incorrectly identified as discontinuous boundary points are removed using a density-based outlier detection technique. The effectiveness of the two-step algorithm is demonstrated on real-world data acquired using a FARO® LS 880 laser scanner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".