Tracking a Depth Camera: Parameter Exploration for Fast ICP
Why this work is in the frame
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Bibliographic record
Abstract
The increasing number of ICP variants leads to an explosion of algorithms and parameters.This renders difficult the selection of the appropriate combination for a given application.In this paper, we propose a state-of-the-art, modular, and efficient implementation of an ICP library.We took advantage of the recent availability of fast depth cameras to demonstrate one application example: a 3D pose tracker running at 30 Hz.For this application, we show the modularity of our ICP library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds.This tracker is then evaluated using datasets recorded along a ground truth of millimeter accuracy.We provide both source code and datasets to the community in order to accelerate further comparisons in this field.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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