Two Models for Surface Segmentation using the Total Variation of the Normal Vector
This work addresses surface segmentation for computer graphics or geometry processing, presenting an incremental improvement in regularization methods.
The authors tackled surface segmentation on triangular meshes by proposing a variational approach with two total variation-based regularizers, finding that the second regularizer, which penalizes total variation in label space, performed better by removing noise more reliably in constant curvature regions.
We consider the problem of surface segmentation, where the goal is to partition a surface represented by a triangular mesh. The segmentation is based on the similarity of the normal vector field to a given set of label vectors. We propose a variational approach and compare two different regularizers, both based on a total variation measure. The first regularizer penalizes the total variation of the assignment function directly, while the second regularizer penalizes the total variation in the label space. In order to solve the resulting optimization problems, we use variations of the split Bregman (ADMM) iteration adapted to the problem at hand. While computationally more expensive, the second regularizer yields better results in our experiments, in particular it removes noise more reliably in regions of constant curvature.