Synthetically Trained 3D Visual Tracker of Underwater Vehicles
Bibliographic record
Abstract
We present a method for visually detecting and tracking the 3D pose of autonomous underwater vehicles, which aims to enable robust multi-robot convoying. We follow the approach of tracking-by-detection, which combines the robust, drift-free nature of object detection with the temporal consistency of tracking algorithms. Central to our method is a multi-output convolutional network that jointly predicts whether the target robot is present in the image (classification), the 2D bounding box around the target in the image plane, and the 3D orientation of the target. This, combined with camera intrinsic parameters and prior knowledge of the robot's absolute scale, allows us to recover the full 6-degree-of-freedom pose (translation and orientation) of the target robot. To train our network, we use only synthetic images rendered using the Unreal game engine, which is a cost-effective way to produce a large training set without the need for laborious manual annotations. Our evaluation analyzes the impact of orientation offset on 3D detection accuracy, and demonstrates successful generalization of the learned model to real underwater photographs of the target robot.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".