Bibliographic record
Abstract
The vision and robotics communities have developed different methods for object pose estimation, all of which have their disadvantages and advantages. A popular method saves all possible object model images from different viewpoints and their 2D-to-3D correspondences in database off-line. Then local feature matching is applied between the current view and the model images in the database. For the top matched image, the approach of a PnP algorithm followed by RANSAC is used to estimate object pose. Such a method has good accuracy, but lacks efficiency, consuming O(MN2) time where N and M are the number of features in a model and the number of models, respectively. To tackle this problem, we propose a method that improves the efficiency in two ways. First, we employ a hierarchical clustering method to find the proper number of model images to represent each object, leading to a decrease in M. Second, a coase-to-fine object pose estimation method is proposed, to decrease the time to find the best matching model image. Specifically, in the coarse step, given an image, the most similar model image is retrieved using a global image descriptor, which we compute using a pre-trained deep neural network. Then in the fine step, a local descriptor feature matching method is applied to find matching keypoints between current image and the model image found in the coarse step. Finally, with pre-registered 2D-to-3D correspondences for each model, an accurate object pose is calculated using the PnP and RANSAC approach. The performance of our method is evaluated on the Amazon Picking Challenge dataset.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".