With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …

Target localization and classification from radar point clouds is a challenging task due to the
inherently sparse nature of the data with highly non-uniform target distribution. This work …

This paper presents a Boundary Representation (BREP) compatible data representation for
Graph Neural Network (GNN) based feature identification. This data representation follows …

With the development of science and technology, virtual reality and other technologies have
gradually developed and popularized, sprouting a series of interdisciplinary, such as …

3D point cloud semantic segmentation has been widely used in industrial scenes and has
attracted continuous attention as a critical technology for understanding the intelligent robot …

Fully exploring the correlation of local features and their spatial distribution in point clouds is
essential for feature modeling. This paper, inspired by convolutional neural networks …

With the exponential increasement of 3D models, 3D model classification is crucial to the
effective management and retrieval of model database. Feature descriptor has important …

Among all usual formats of representing 3D objects, including depth image, mesh and
volumetric grid, point cloud is the most commonly used and preferred format, because it …

Convolutional neural networks, as a branch of deep neural networks, have been widely
used in multidimensional signal processing, especially in point cloud signal processing …

Both pedestrians and cyclists as vulnerable road users (VRUs) always exhibit agile and
complex behaviors whose safety protection has attracted the highest priority concerns in the …