Multiscale-Hashing Network for 6-D Pose Estimation of Unseen Objects in the Wild
Bibliographic record
Abstract
Estimating the pose of unseen objects is a fundamental task in robotics and industrial automation. Some methods rely on prior knowledge of individual objects for this task and require the model to be trained on specific object instances or categories. Other methods can estimate the pose of unseen objects but are often limited in handling occlusions. To address these challenges, we propose an unseen object pose estimation method for objects not encountered during training. We propose a Hashing Attention Network that integrates features from multiple scales to effectively capture global information while maintaining high sensitivity to local positional details, thereby significantly enhancing the model’s predictive accuracy for occluded objects. We incorporate Locality Sensitive Hashing into the self-attention mechanism of the vision transformer. This approach significantly reduces computational complexity by computing attention for tokens at each scale based on hash similarity, both within the same scale and across multiple scales. The network was trained on selected BOP datasets, and the Linemod, T-LESS, and Wild6D datasets were used to evaluate pose estimation on unseen objects. Our results show that the proposed model outperforms existing methods across various quantitative metrics, making it well suited for industrial applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".