INTEGRATION OF TERRESTRIAL LASER SCANNING POINTS AND 2D FLOOR PLANS BASED ON MAXIMUM SEQUENTIAL SIMILARITY
Bibliographic record
Abstract
Abstract. This paper presents a Maximum Sequential Similarity Reasoning (MSSR) algorithm based method for co-registration of 3D TLS data and 2D floor plans. The co-registration consists of two tasks: estimating a transformation between the two datasets and finding the vertical locations of windows and doors. The method first extracts TLS line sequences and floor plan line sequences from a series of horizontal cross-section bands of the TLS points and floor plans respectively. Then each line sequence is further decomposed into column vectors defined by using local transformation invariant information between two neighbouring line segments. Based on a normalized cross-correlation based similarity score function, the proposed MSSR algorithm is then used to iteratively estimate the vertical and horizontal locations of each floor plan by finding the longest matched consecutive column vectors between floor plan line sequences and TLS line sequences. A group matching algorithm is applied to simultaneously determine final matching results across floor plans and estimate the transformation parameters between floor plans and TLS points. With real datasets, the proposed method demonstrates its ability to deal with occlusions and multiple matching problems. It also shows the potential to detect conflict between floor plan and as-built, which makes it a promising method that can find many applications in many industrial fields.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".