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 …

Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding
autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

At present, effectively aggregating and transferring the local features of point cloud is still an
unresolved technological conundrum. In this study, we propose a new space-cover …

Point cloud sampling is a less explored research topic for this data representation. The most
commonly used sampling methods are still classical random sampling and farthest point …

As 3D point cloud analysis has received increasing attention, the insufficient scale of point
cloud datasets and the weak generalization ability of networks become prominent. In this …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

Transformers have been recently explored for 3D point cloud understanding with impressive
progress achieved. A large number of points, over 0.1 million, make the global self-attention …

The success of the transformer architecture in natural language processing has recently
triggered attention in the computer vision field. The transformer has been used as a …

In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …