Experimental Study of CAD‐Based Scaled Alignment and Object Pose Estimation for RGB‐D Sensor
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".