Near Real-Time 3D Reconstruction and Quality 3D Point Cloud for Time-Critical Construction Monitoring
Bibliographic record
Abstract
Improving the rapidity of 3D reconstruction is vital for time-critical construction tasks such as progress monitoring and hazard detection, but the majority of construction studies in this area have focused on improving its quality. We applied a Direct Sparse Odometry with Loop Closure (LDSO)-based 3D reconstruction method, improving the existing algorithm and tuning its hyper-parameter settings, to achieve both near real-time operation and quality 3D point cloud simultaneously. When validated using a benchmark dataset, the proposed method showed notable improvement in 3D point cloud density, as well as loop closure robustness, compared to the original LDSO. In addition, we conducted a real field test to validate the tuned LDSO’s accuracy and speed at both object and site scales, where we demonstrated our method’s near real-time operation and capability to produce a quality 3D point cloud comparable to that of the existing method. The proposed method improves the accessibility of the 3D reconstruction technique, which in turn helps construction professionals monitor their jobsite safety and progress in a more efficient manner.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".