Neural Shape Diameter Function for Efficient Mesh Segmentation. (arXiv:2306.11737v1 [cs.GR])

Partitioning a polygonal mesh into meaningful parts can be challenging. Many
applications require decomposing such structures for further processing in
computer graphics. In the last decade, several methods were proposed to tackle
this problem, at the cost of intensive computational times. Recently, machine
learning has proven to be effective for the segmentation task on 3D structures.
Nevertheless, these state-of-the-art methods are often hardly generalizable and
require dividing the learned model into several specific classes of objects to
avoid overfitting. We present a data-driven approach leveraging deep learning
to encode a mapping function prior to mesh segmentation for multiple
applications. Our network reproduces a neighborhood map using our knowledge of
the textsl{Shape Diameter Function} (SDF) method using similarities among
vertex neighborhoods. Our approach is resolution-agnostic as we downsample the
input meshes and query the full-resolution structure solely for neighborhood
contributions. Using our predicted SDF values, we can inject the resulting
structure into a graph-cut algorithm to generate an efficient and robust mesh
segmentation while considerably reducing the required computation times.