Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Applications of machine learning techniques for materials modeling typically involve
functions that are known to be equivariant or invariant to specific symmetries. While graph …

GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …

Objectives Herbal prescription recommendation (HPR) is a hot topic and challenging issue
in field of clinical decision support of traditional Chinese medicine (TCM). However, almost …

In this paper, we propose a fully differentiable pipeline for estimating accurate dense
correspondences between 3D point clouds. The proposed pipeline is an extension and a …

Transfer learning is fundamental for addressing problems in settings with little training data.
While several transfer learning approaches have been proposed in 3D, unfortunately, these …

3D shape generation is a fundamental operation in computer graphics. While significant
progress has been made, especially with recent deep generative models, it remains a …

A large number of 3D spectral descriptors have been proposed in the literature, which act as
an essential component for 3D deformable shape matching and related applications. An …

With recent success of deep learning in 2-D visual recognition, deep-learning-based 3-D
point cloud analysis has received increasing attention from the community, especially due to …

3D shapes are widely used in computer graphics and computer vision, with applications
ranging from modeling, recognition to rendering. Synthesizing high-quality shapes is …