Arbitrary 3D Object Extraction from Cluttered Laser Scans Using Local Features
Bibliographic record
Abstract
Determination of construction performance metrics requires intensive processing of large amounts of data collected on construction sites including cluttered laser scans.For example, for quality control of construction components using 3D laser scans, the acquired point cloud should be cleaned and the object-of-interest should be extracted for measuring the incurred deviations.Such a procedure is tedious, time consuming and inaccurate due to intensive manual user operations.Although automatic extraction of rough and simple 3D shapes and features is performed by applying techniques such as Hough transform, automatic extraction of construction components with complex geometry is a challenging research need that must be addressed for fully automated modelling and processing.This paper presents a framework for automated extraction of 3D objects with arbitrary shapes and geometry.A new local feature set, which is globally invariant, is created in order to represent 3D models.The feature space created is then searched for in the cluttered laser scan by hashing from a hash table created for the 3D model.The best match is then extracted automatically by applying a post-processing RANSAC loop.The framework is then followed by an ICP-based registration in order to refine the best match identified.The results show that the method is sufficiently robust and quick to be applied for effective and efficient post processing of the laser scans acquired on construction sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".