Automating adaptive scan planning for static laser scanning in complex 3D environments
Why this work is in the frame
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Bibliographic record
Abstract
Laser scanning is increasingly important for applications in the built environment and requires efficient planning for effective data collection. The conventional process is a time-consuming and repetitive manual task, presenting a strong case for automation. This paper introduces a novel method for scan planning in complex 3D environments, overcoming the limitations of existing approaches. It accepts any 3D model or point cloud as input by processing them as triangulated meshes. The method involves automated steps of mesh processing, viewpoint candidate generation, and evaluations of visibility and coverage. It facilitates optimized planning for static laser scanning missions by selecting appropriate viewpoints while considering targetless registration needs. Our method can handle uniform coverage requirements and specific local requirements. It is rigorously tested through method comparisons and extensive parameter studies and applied to several scenes, including a comparison to scan strategies manually created by laser scanning experts, demonstrating its practical applicability. • A novel method for automated planning of static laser scanning in complex 3D environments • Identifies efficient scanning strategies with suitable locations and sequence in the scene • Works based on a triangulated mesh representation that can be derived from any 3D scene • Deterministic evaluation of occlusions, incidence angles, point densities, and overlaps • Achieves better coverage and efficiency than manual solutions provided by experts.
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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 | 0.000 | 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 it