Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …

The simulation of production processes using a digital twin can be utilized for prospective
planning, analysis of existing systems or process-parallel monitoring. In all cases, the digital …

Partial point cloud registration aims to transform partial scans into a common coordinate
system. It is an important preprocessing step to generate complete 3D shapes. Although …

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 …

In this paper, we propose the multi-view saliency guided deep neural network (MVSG-DNN)
for 3D object retrieval and classification. This method mainly consists of three key modules …

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success
of deep learning on images and the increased availability of 3D data. In this paper, we aim …

In multi-view 3D object retrieval tasks, it is pivotal to aggregate visual features extracted from
multiple view images to generate a discriminative representation for a 3D object. The …

View-based methods have achieved state-of-the-art performance in 3D object retrieval.
However, view-based methods still encounter two major challenges. The first is how to …

Processing large amount of high-resolution 3D data requires enormous computational
resources. As a result, a suitable 3D data representation must be chosen, and the data must …