Marker‐less real‐time intra‐operative camera and hand‐eye calibration procedure for surgical augmented reality
Bibliographic record
Abstract
Accurate medical Augmented Reality (AR) rendering requires two calibrations, a camera intrinsic matrix estimation and a hand-eye transformation. We present a unified, practical, marker-less, real-time system to estimate both these transformations during surgery. For camera calibration we perform calibrations at multiple distances from the endoscope, pre-operatively, to parametrize the camera intrinsic matrix as a function of distance from the endoscope. Then, we retrieve the camera parameters intra-operatively by estimating the distance of the surgical site from the endoscope in less than 1 s. Unlike in prior work, our method does not require the endoscope to be taken out of the patient; for the hand-eye calibration, as opposed to conventional methods that require the identification of a marker, we make use of a rendered tool-tip in 3D. As the surgeon moves the instrument and observes the offset between the actual and the rendered tool-tip, they can select points of high visual error and manually bring the instrument tip to match the virtual rendered tool tip. To evaluate the hand-eye calibration, 5 subjects carried out the hand-eye calibration procedure on a da Vinci robot. Average Target Registration Error of approximately 7mm was achieved with just three data points.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".