Open source video-based hand-eye calibration
Bibliographic record
Abstract
Augmented reality is becoming prevalent in modern video-based surgical navigation systems. Augmented reality in forms of image-fusion between the virtual objects (i.e. virtual representation of the anatomy derived from pre-operative imaging modalities) and the real objects (i.e. anatomy imaged by a spatially-tracked surgical camera) facilitate the visualization and perception of the surgical scene. However, this requires spatial calibration between the external tracking system and the optical axis of the surgical camera, known as hand-eye calibration. With the standard implementation of the most common hand-eye calibration techniques being static-photo-based, the time required for data collection may inhibit the thoroughness and robustness to achieve an accurate calibration. To address these translational issues, we introduce a video-based hand-eye calibration technique with open-source implementation that is accurate and robust. Based on the point-to-line Procrustean registration, a short video of a tracked and pivot-calibrated ball-tip stylus was recorded where, in each frame of the tracked video, the 3D position of the ball-tip (point) and its projection onto the video (line) serve as a calibration data point. We further devise a data sampling mechanism designed to optimize the spatial configuration of the calibration fiducials, leading to consistently high quality hand-eye calibrations. To demonstrate the efficacy of our work, a Monte Carlo simulation was performed to obtain the mean target projection error as a function of the number of calibration data points. The results obtained, exemplified using a Logitech C920 Pro HD Webcam with an image resolution of 640 × 480, show that the mean projection error decreased as more data points were used per calibration, and the majority of mean projection errors fell below four pixels. An open-source implementation, in the form of a 3D Slicer module, is available on GitHub.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".