Efficient and reliable template set matching for 3D object recognition
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
Object recognition in range image data is formulated as template set matching. The object model is represented as a set of voxel templates, one for each possible pose. The set of all templates is composed into a binary decision tree. Each leaf node references a small number of templates. Each internal node references a single voxel, and has two branches, T and F. The subtree branching from the T branch contains the subset of templates which contain the node voxel. Conversely, the subtree branching from F branch contains the subset of templates which do not contain the node voxel. Traversing the tree at any image location executes a point probe strategy. It efficiently determines a good match with the template set by interrogating only those elements which discriminate between the remaining possible interpretations. The method has been implemented for a number of different heuristic tree design and traversal methods. Results are presented of extensive tests for two objects under isolated, cluttered, and occluded scene conditions. It is shown that there exist traversal/design combinations which are both efficient and reliable, and that the method is robust.
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.
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 | 0.000 |
| 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 it