MorphModes: Non-rigid Registration via Adaptive Skinning Eigenmodes

TL;DR

This paper introduces MorphModes, a robust non-rigid registration method using adaptive skinning eigenmodes and SDF matching, outperforming traditional ICP-based approaches in handling complex deformations.

Contribution

It proposes a novel SDF-based optimization framework with a skinning eigenmode subspace and adaptive scheme, enhancing robustness and reducing parameter sensitivity.

Findings

More robust registration than NRICP

Handles localized deformations effectively

Less sensitive to initial conditions

Abstract

Non-rigid registration is a crucial task with applications in medical imaging, industrial robotics, computer vision, and entertainment. Standard approaches accomplish this task using variations on the Non-Rigid Iterative Closest Point (NRICP) algorithms, which are prone to local minima and sensitive to initial conditions. We instead formulate the non-rigid registration problem as a Signed Distance Function (SDF) matching optimization problem, which provides richer shape information compared to traditional ICP methods. To avoid degenerate solutions, we propose to use a smooth Skinning Eigenmode subspace to parameterize the optimization problem. Finally, we propose an adaptive subspace optimization scheme to allow the resolution of localized deformations within the optimization. The result is a non-rigid registration algorithm that is more robust than NRICP, without the parameter sensitivity present in other SDF-matching approaches.