3D Disaster Scene Reconstruction Using a Canine-Mounted RGB-D Sensor
Why this work is in the frame
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Bibliographic record
Abstract
A 3D map of the interior of a disaster site that pinpoints the location of trapped victims would greatly aid search and rescue efforts. We propose using a canine-mounted RGB-D sensor; a trained rescue dog can carry an image sensor through the site to build a 3D model useful for rescuers. However, the registration of the data provides challenges beyond those typically faced in scene reconstruction due to the rapid motion and sudden pose changes. We provide a solution whereby a pre-processing step identifies good frames to combine from a stream of RGB-D image frames. These selected images are then combined into the larger model by calculating a relative pose using the 3D location of key points matched in the visible images. Results are presented of 3D models constructed using data collected from the canine platform.
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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) | 0.001 | 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