# Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity

## TL;DR

This paper introduces a robust non-rigid registration method that employs reweighted sparsity on position and transformation, effectively handling noise and outliers in 3D shape alignment.

## Contribution

It proposes a novel double sparsity model with reweighting scheme for improved robustness and guarantees convergence through an optimized subproblem approach.

## Key findings

- Outperforms state-of-the-art methods in accuracy.
- More robust to noise and outliers.
- Effective on both public and real datasets.

## Abstract

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.