Weakly-Supervised Depth Completion during Robotic Micromanipulation from a Monocular Microscopic Image
Why this work is in the frame
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Bibliographic record
Abstract
Obtaining three-dimensional information, especially the z-axis depth information, is crucial for robotic micromanipulation. Due to the unavailability of depth sensors such as lidars in micromanipulation setups, traditional depth acquisition methods such as depth from focus or depth from defocus directly infer depth from microscopic images and suffer from poor resolution. Alternatively, micromanipulation tasks obtain accurate depth information by detecting the contact between an end-effector and an object (e.g., a cell). Despite its high accuracy, only sparse depth data can be obtained due to its low efficiency. This paper aims to address the challenge of acquiring dense depth information during robotic cell micromanipulation. A weakly-supervised depth completion network is proposed to take cell images and sparse depth data obtained by contact detection as input to generate a dense depth map. A two-stage data augmentation method is proposed to augment the sparse depth data, and the depth map is optimized by a network refinement method. The experimental results show that the MAE value of the depth prediction error is less than 0.3 µm, which proves the accuracy and effectiveness of the method. This deep learning network pipeline can be seamlessly integrated with the robotic micromanipulation tasks to provide accurate depth information.
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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