SHREC'10 Track: Non-rigid 3D Shape Retrieval
Bibliographic record
Abstract
Non-rigid 3D objects are commonly seen in our surroundings. However, previous efforts have been mainly devoted to the retrieval of rigid 3D models, and thus comparing non-rigid 3D shapes is still a challenging problem in content-based 3D object retrieval. Therefore, we organize this track to promote the development of non-rigid 3D shape retrieval.The objective of this track is to evaluate the performance of 3D shape retrieval approaches on the subset of a publicly available non-rigid 3D models database----McGill Articulated Shape Benchmark database.Task description: The task is to evaluate the dissimilarity between every two objects in the database and then output the dissimilarity matrix.Data set: The McGill Articulated Shape Benchmark database consists of 255 non-rigid 3D models which are classified into 10 categories. The maximum number of the objects in a class is 31, while the minimum number is 20.200 models are selected (or modified) to generate our test database to ensure that every class contains equal number of models. The models are represented as watertight triangle meshes and the file format is selected as the ASCII Object File Format (*.off). The original database is publicly available on the website: http://www.cim.mcgill.ca/~shape/benchMark/Evaluation Methodology: We will employ the following evaluation measures: Precision-Recall curve; Average Precision (AP) and Mean Average Precision (MAP); E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2).Please Cite the paper: SHREC'10 Track: Non-rigid 3D Shape Retrieval., Z. Lian, A. Godil, T. Fabry, T. Furuya, J. Hermans, R. Ohbuchi, C. Shu, D. Smeets, P. Suetens, D. Vandermeulen, S. Wuhrer In: M. Daoudi, T. Schreck, M. Spagnuolo, I. Pratikakis, R. Veltkamp (eds.), Proceedings of the Eurographics/ACM SIGGRAPH Symposium on 3D Object Retrieval, 2010.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".