A Comparative Survey on 3D Models Retrieval Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In computer vision many studies have been conducted in order to perform the matching and comparison of 3D models of objects. The main goal of matching is to group the models into different categories according to their similarity in order to allow their retrieval for recognition purposes and for further usage. So, in most of the cases, the comparison is run on a large dataset containing various models whether they belong to the same type of object or not and generally having similar or different shapes and poses. The objects’ nature and characteristics are important factors to be taken into consideration before performing the comparison step. We distinguish between two main categories of objects: rigid objects and deformable objects whose treatment and handling differ in the modeling as well as in the comparison phases. In this paper, we will be focusing on the comparison of deformable objects, and thus dealing with objects whose shapes might vary in different instances. For this purpose two main approaches used in the retrieval of 3D deformable models will be reviewed and implemented: the spectral approach and the bag-of-features approach. The deformation or variation in shape involves different aspects depending on the type of object. It could be a change in the posture of an articulated or bendable model, or it could result from a variation (loss or gain) in the total mass leading to a change in the surface and thus in the shape of the object. Even more complex situations occur when both cases are combined together.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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