Experimental Study of CAD‐Based Scaled Alignment and Object Pose Estimation for RGB‐D Sensor
Why this work is in the frame
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Bibliographic record
Abstract
Pose estimation of objects is one of the main tasks for robotics to understand their environments and for monitoring tasks of grasping and manipulation of objects. In this paper, we present an experimental study of CAD‐based object pose estimation to detect object locations and estimate their orientations using the prior defined models. Specifically, our study pipeline is developed for RGB‐D sensors and consists of three steps. First, we incorporate an objection detection method using RGB images, which can result in the definition of the bounding boxes, instance masks, and class labels of detected objects with missing pose information. Then, we leverage the depth values of the masked pixels and known camera intrinsics to generate point clouds of objects. Finally, we align CAD models, defined in canonical poses, to the scan objects, achieving pose estimation and complete representation for the objects. Given that there may exist many challenges for such alignment task such as scale differences, partial overlap, noise, and outliers, we introduce two alignment approaches, namely, scale iterative closest point (SICP) and coherent point drift (CPD), and present a comprehensive experimental study of their accuracy, robustness, and computational efficiency. In particular, we observe that the methods are sensitive to the initial relative poses of objects. To address this problem, we introduce a multipose initialization scheme to improve their robustness. Our experimental results show that both methods can achieve accurate alignment; however, scale ICP (SICP) is time‐efficient, while CPD is more robust to noise and occlusions. Our study demonstrates the feasibility of using RGB‐D sensors, an object detection module, and point cloud alignment methods for accurate object detection and pose estimation.
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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 | 0.000 | 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