Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery
3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preoperative mesh. Because ground-truth transformations are unavailable for real data, supervised networks are trained on synthetic organ pairs. At test time, real reconstructions differ from synthetic data and are noisy, sparse, and occluded, which degrades correspondence estimation. Test-time adaptation (TTA) can reduce this domain shift, but existing methods mainly rely on logits, entropy, class prototypes, or cache memories unavailable in registration. Registration also involves paired inputs with an asymmetric shift that primarily affects t...
| Authors | Nina Bodelot, Soufiane Belharbi, Eric Granger |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 23 September 2026 |
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