Symmetry‐Aware Mesh Segmentation into Uniform Overlapping Patches

Abstract

We present intrinsic methods to address the fundamental problem of segmenting a mesh into a specified number of patches with a uniform size and a controllable overlap. Although never addressed in the literature, such a segmentation is useful for a wide range of processing operations where patches represent local regions and overlaps regularize solutions in neighbour patches. Further, we propose a symmetry‐aware distance measure and symmetric modification to furthest‐point sampling, so that our methods can operate on semantically symmetric meshes. We introduce quantitative measures of patch size uniformity and symmetry, and show that our segmentation outperforms state‐of‐the‐art alternatives in experiments on a well‐known dataset. We also use our segmentation in illustrative applications to texture stitching and synthesis where we improve results over state‐of‐the‐art approaches.We present intrinsic methods to address the fundamental problem of segmenting a mesh into a specified number of patches with a uniform size and a controllable overlap. Although never addressed in the literature, such a segmentation is useful for a wide range of processing operations where patches represent local regions and overlaps regularize solutions in neighbour patches. Further, we propose a symmetry‐aware distance measure and symmetric modification to furthest‐point sampling, so that our methods can operate on semantically symmetric meshes. We introduce quantitative measures of patch size uniformity and symmetry, and show that our segmentation outperforms state‐of‐the‐art alternatives in experiments on a well‐known dataset.