Comparison of Local Visual Feature Detectors and Descriptors for the Registration of 3D Building Scenes
Bibliographic record
Abstract
Three-dimensional (3D) as-built geometric models are useful for many building assessment and management tasks. However, the current process of creating such models is labor-intensive. A significant amount of manual work is required to register the remote sensing data captured from multiple scans into one scene (i.e., scene registration). To automate the registration work, several research studies have been developed to automate the registration process by the detection and matching of common visual features in consecutive scans. This paper investigates the effectiveness of different combinations of common visual feature detectors and descriptors that have been widely used in the scene registration of 3D buildings. The evaluation criteria include registration accuracy and speed. The feature detectors and descriptors have been tested in a total of 31 realistic building scenarios. The results show that the combination of the scale-invariant feature transform feature detector and descriptor reached more accurate results than the others. The fastest speed is achieved by the use of an oriented binary robust independent elementary features (ORB) detector in combination with the speeded-up robust features or ORB descriptor.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".