Point Cloud Registration in Laparoscopic Liver Surgery Using Keypoint Correspondence Registration Network
Bibliographic record
Abstract
Laparoscopic liver surgery is a newly developed minimally invasive technique and represents an inevitable trend in the future development of surgical methods. By using augmented reality (AR) technology to overlay preoperative CT models with intraoperative laparoscopic videos, surgeons can accurately locate blood vessels and tumors, significantly enhancing the safety and precision of surgeries. Point cloud registration technology is key to achieving this effect. However, there are two major challenges in registering the CT model with the point cloud surface reconstructed from intraoperative laparoscopy. First, the surface features of the organ are not prominent. Second, due to the limited field of view of the laparoscope, the reconstructed surface typically represents only a very small portion of the entire organ. To address these issues, this paper proposes the keypoint correspondence registration network (KCR-Net). This network first uses the neighborhood feature fusion module (NFFM) to aggregate and interact features from different regions and structures within a pair of point clouds to obtain comprehensive feature representations. Then, through correspondence generation, it directly generates keypoints and their corresponding weights, with keypoints located in the common structures of the point clouds to be registered, and corresponding weights learned automatically by the network. This approach enables accurate point cloud registration even under conditions of extremely low overlap. Experiments conducted on the ModelNet40, 3Dircadb, DePoLL demonstrate that our method achieves excellent registration accuracy and is capable of meeting the requirements of real-world scenarios.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".