Category-Level Pose Estimation and Iterative Refinement for Monocular RGB-D Image
Why this work is in the frame
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Bibliographic record
Abstract
Category-level pose estimation is proposed to predict the 6D pose of objects under a specific category and has wide applications in fields such as robotics, virtual reality, and autonomous driving. With the development of VR/AR technology, pose estimation has gradually become a research hotspot in 3D scene understanding. However, most methods fail to fully utilize geometric and color information to solve intra-class shape variations, which leads to inaccurate prediction results. To solve the above problems, we propose a novel pose estimation and iterative refinement network, use an attention mechanism to fuse multi-modal information to obtain color features after a coordinate transformation, and design iterative modules to ensure the accuracy of object geometric features. Specifically, we use an encoder-decoder architecture to implicitly generate a coarse-grained initial pose and refine it through an iterative refinement module. In addition, due to the differences between rotation and position estimation, we design a multi-head pose decoder that utilizes the local geometry and global features. Finally, we design a transformer-based coordinate transformation attention module to extract pose-sensitive features from RGB images and supervise color information by correlating point cloud features in different coordinate systems. We train and test our network on the synthetic dataset CAMERA25 and the real dataset REAL275. Experimental results show that our method achieves state-of-the-art performance on multiple evaluation metrics.
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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 | 0.001 | 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