Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Abstract Text, 2D, and 3D information are crucial information representations in modern
science and management disciplines. However, complex and irregular 3D data produce …

Abstract 3D shape processing is a fundamental computer application. Specifically, 3D mesh
could provide a natural and detailed way for object representation. However, due to its non …

Shape analysis tasks, including mesh classification, segmentation, and retrieval
demonstrate symmetries in Euclidean space and should be invariant to geometric …

Textured 3-D mesh is one of the final user products in photogrammetry and remote sensing.
However, research on the semantic segmentation of complex urban scenes represented by …

Deep neural networks (DNNs) have been widely used for mesh processing in recent years.
However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most …

Despite significant advances in 3D mesh segmentation techniques driven by deep learning,
segmenting 3D meshes without exhaustive manual labeling remains a challenging due to …

Triangular meshes are widely used to represent three-dimensional objects. As a result,
many recent works have addressed the need for geometric deep learning on 3D meshes …

The superior performance of deep learning in different domains has sparked significant
interest in its applicability to 3D computer graphics. Deep learning has become the dominant …

The segmentation of 3D shapes is a critical aspect of shape analysis. However, most
existing methods for 3D shape segmentation treat each face of the original mesh model with …