Bibliographic record
Abstract
This paper presents the automated model synthesis component of an integrated passive 3D vision system. The synthesized models can be used by the object recognition and pose determination components. The model synthesis obtains the 3D object model from images acquired by a CCD camera posed at various known positions. This paper presents developments and discusses automatic model synthesis. The tasks include robust 2D feature detection; 2D feature post-processing for eliminating noise and recovering missing features; 2D feature grouping of structurally related 2D features; stereo triangulation with a new form of epipolar line constraint; projective inversion of ellipses; synthesis for circular shape in 3D space from its projective views based on the ellipse pose hypothesis; and incremental model synthesis of model from multiple views based on the vertex triangulation. To demonstrate full automation, we use a single CCD camera mounted on the last link of a robot arm. The integrated robot system is able to move the CCD camera around the object and capture images at various vantage points and furnish the camera pose corresponding to each image acquired. The intelligent system then synthesizes the extracted features from each image to obtain a 3D model of the object. Such an approach, though more difficult than the direct use of range data through range sensors, is of great importance for space and industrial automation where cost and flexibility are of concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".