Eyes on the Prize: Improved Biological Surface Registration via Forward Propagation

Understand

Many algorithms for surface registration risk producing significant errors if surfaces are significantly nonisometric.

• Manifold learning has been shown to be effective at improving registration quality, using information from an entire collection of surfaces to correct issues present in pairwise registrations.
• These methods, however, are not robust to changes in the collection of surfaces, or do not produce accurate registrations at a resolution high enough for subsequent downstream analysis.
• We propose a novel algorithm for efficiently registering such collections given initial correspondences with varying degrees of accuracy.