3D Models' retrieval system design based on Poisson's histogram of 2D selective views
Bibliographic record
Abstract
In this paper, we provide an effective new 3D models' retrieval system based on Poisson equation. Usually, 3D models have considered in two major kinds: directly in 3D space and indirectly by a set of 2D views that extracted from that model. Compared with the obtained results of different kind of these kinds of methods, we try to design our 3D models' retrieval system on special 2D views that select by using Kmeans clustering method that uses a set of geometric features of each silhouette to result the most discriminating views. This will be great helpful to improve any 3D model retrieval system performance. After finding the best views of each 3D model, we will use the Poisson equation to define 2D views' shape signature that used in a retrieval system in histogram form. We use the 3D shapes of McGill database to verify the performance of our proposed 3D models' retrieval. The simulation results show that proposed method is better performance rather than some existing 2D view and histogram based shape descriptors in retrieving correctness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".