Development of Continuum Shape Constraint Analysis (CSCA) for Computer Vision Applications Using Range Data
Bibliographic record
Abstract
This paper further presents continuum shape constraint analysis (CSCA) of surfaces. CSCA is a generalization of discrete-point based constraint analysis which can be used to predict performance of registration algorithms. A surface-based self-registration cost function to which constraint analysis can be applied is introduced. This cost function takes into account a direction the object is viewed at. A sample study is provided to illustrate this approach applied to the problem of pose estimation using range-data taken from a scanning instrument such as LIDAR. Specifically, CSCA is used to assess an object feature for suitability for local LIDAR scanning and subsequent application of the ICP (iterative closest-point) algorithm to determine pose. In this study, the constraint analysis uses noise amplification index (NAI) as an output measure. The continuum nature of the CSCA approach renders the registration cost matrix and the derived NAI as pure shape properties of the feature with a dependence on viewpoint.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".