A Stereo-Based System with Inertial Navigation for Outdoor 3D Scanning
Bibliographic record
Abstract
In this paper, we introduce a 3D scanning system consisting of a stereo camera combined with an inertial navigation system. We employ this system to explore the suitability of the standard 3D scanning pipeline when working with stereo range data of objects and architecture in an outdoor setting. Our range scanning system is very mobile without any mechanical actuators. We rely solely on the inertial navigation system for coarse registration with out any cumbersome manual procedure or scene dependent visual feature tracking. Our detail registration is a variant of the Iterative Closest Point algorithm. We introduce a set of heuristics that aims to produce reliable and repeatable convergence despite the diversity of the objects, and types of motions that may be executed by the operator during the scanning process. We also show that in an outdoor setting, range data based on classical binocular stereo can be effective for 3D modeling. Our system produces results of only slightly lower quality than an indoor triangulation scanner at a much lower system cost.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".