Relief Pattern Segmentation Using 2D-Grid Patches on a Locally Ordered Mesh Manifold (Ohne Sprachangabe)

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The mesh manifold support has been analyzed to perform several different tasks. Recently, it emerged the need for new methods capable of analyzing relief patterns on the surface. In particular, a new and not investigated problem is that of segmenting the surface according to the presence of different relief patterns. In this paper, we introduce this problem and propose a new approach for segmenting such relief patterns (also called geometric texture) on the mesh-manifold. Operating on regular and ordered mesh, we design, in the first part of the paper, a new mesh re-sampling technique complying with this requirement. This technique ensures the best trade-off between mesh regularization and geometric texture preservation, when compared with competitive methods. In the second part, we present a novel scheme for segmenting a mesh surface into three classes: texturedsurface, non-textured surface, and edges (i.e., surfaces at the border between the two). This technique leverages the ordered structure of the mesh for deriving 2D-grid patches allowing us to approach the segmentation problem as a patch-classification technique using a CNN network in a transfer learning setting. Experiments performed on surface samples from the SHREC'18 contest show remarkable performance with an overall segmentation accuracy of over 99%.

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