Consistent segment-wise matching with multi-layer graphs.

Segment-wise matching is an important research problem that supports higher-level understanding of shapes in geometry processing. Many existing segment-wise matching techniques assume perfect input segmentation, and would suffer from imperfect or over-segmented input. To handle this shortcoming, we propose multi-layer graphs (MLGs) to represent possible arrangements of partially merged segments of input shapes. We then adapt the diffusion pruning technique on the MLGs to find consistent segment-wise matching. To obtain high quality matching, we develop a voting step to find hierarchically consistent correspondences as final output. We evaluate our technique with both qualitative and quantitative experiments on both man-made and deformable shapes. Experimental results demonstrate the effectiveness of our technique when compared to two state-of-the-art methods.