A Method for Dynamic Selection of Optimal Depth Measurements Acquisition with Random Access Range Sensors
Bibliographic record
Abstract
It is well established that acquiring large amount of range data with vision sensors can quickly lead to important data management challenges where processing capabilities become saturated and preempt full usage of the information available for autonomous systems to make educated decisions. While sub-sampling offers a naive solution for reducing dataset dimension after acquisition, it does not capitalize on the knowledge available in already acquired data to selectively and dynamically drive the acquisition process over the most significant regions in a scene, the latter being generally characterized by variations in depth and surface shape. This paper discusses the development of a formal improvement measure and a method to automatically establish which regions within the field of view of a range sensor would provide the most improvement to a model of the scene if further acquisitions were concentrated in priority over those regions. The proposed algorithm mainly targets applications using random access range sensors, defined as sensors that can acquire depth measurements at specified azimuth and elevation within their field of view. However, the framework is developed to be independent of the range sensing technology used, and is validated with range data acquired from the popular Kinect multi-modal imaging sensor, as well as Neptec,s LMS laser random access range sensor.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".