View Sphere Partitioning via Flux Graphs Boosts Recognition from Sparse Views
Bibliographic record
Abstract
View-based 3D object recognition requires a selection of model object views against which to match a query view. Ideally, for this to be computationally efficient, such a selection should be sparse. To address this problem we partition the view sphere into regions within which the silhouette of a model object is qualitatively unchanged. This is accomplished using a flux-based skeletal representation and skeletal matching to compute the pairwise similarity between two views. Associating each view with a node of a view sphere graph, with the similarity between a pair of views as an edge weight, a clustering algorithm is used to partition the view sphere. Our experiments on exemplar level recognition using 19 models from the Toronto Database and category level recognition using 150 models from the McGill Shape Benchmark demonstrate that in a scenario of recognition from sparse views, sampling model views from such partitions consistently boosts recognition performance when compared against queries sampled randomly or uniformly from the view sphere. We demonstrate the improvement in recognition accuracy for a variety of popular 2D shape similarity approaches: shock graph matching, flux graph matching, shape context based matching and inner distance based matching.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".