Landmark-Free and Scale-Blind Multi-View Registration of Preoperative Liver Models to Intraoperative Surfaces

Key points

• Monocular reconstruction in most cases cannot guarantee scale, albeit rigid-motion registration requires scale as an input.1
• Our proposed framework "SimCompose" estimates the full similarity transformation $\Sim$, including scale, without relying on landmarks and applies the same transformation across multiple views with known relative geometry.1
• For this, we propose a geometry-based selector that evaluates candidate transformations without requiring ground truth.1
• Finally, we propose to use a non-rigid refinement based on a low-dimensional deformation model from Neo-Hookean simulations in the Simulation Open Framework Architecture (SOFA), without additional network training.1

Sources (1)

• [1]Landmark-Free and Scale-Blind Multi-View Registration of Preoperative Liver Models to Intraoperative Surfaces
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:42 PM

  Augmented-reality guidance in laparoscopic liver resection requires aligning preoperative 3D liver models from CT/MRI with intraoperative surfaces.

  Monocular reconstruction in most cases cannot guarantee scale, albeit rigid-motion registration requires scale as an input.

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