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 …

The high-precision generation of 3D building models is a controversial research topic in the
field of smart cities. However, due to the limitations of single-source data, existing methods …

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In
recent years, hypergraph learning has attracted increasing attention due to its flexibility and …

Multi-view projection methods have demonstrated their ability to reach state-of-the-art
performance on 3D shape recognition. Those methods learn different ways to aggregate …

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

Point clouds are among the popular geometry representations for 3D vision applications.
However, without regular structures like 2D images, processing and summarizing …

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model
repositories. Thus, they typically have insufficient geometric details and less informative …

Abstract Domain Adaptation (DA) approaches achieved significant improvements in a wide
range of machine learning and computer vision tasks (ie, classification, detection, and …