Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity
This work addresses robust non-rigid registration for 3-D shape analysis, which is important for applications like computer vision and medical imaging, but it appears incremental as it builds on existing sparsity-based methods with enhancements.
The paper tackles the ill-posed problem of non-rigid registration for 3-D shapes, which is sensitive to noise and outliers, by proposing a method using reweighted position and transformation sparsity, and it outperforms state-of-the-art methods in robustness and accuracy on public and real scanned datasets.
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.