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 …

With the rapid development of sensor technologies and the widespread use of laser
scanning equipment, point clouds, as the main data form and an important information …

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

Recent works on 3D semantic segmentation propose to exploit the synergy between images
and point clouds by processing each modality with a dedicated network and projecting …

Three-dimensional models are widely used in the fields of multimedia, computer graphics,
virtual reality, entertainment, design, and manufacturing because of the rich information that …

The prevalence of relation networks in computer vision is in stark contrast to underexplored
point-based methods. In this paper, we explore the possibilities of local relation operators …

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in
point cloud completion: understanding faithful global shapes from incomplete point clouds …

Due to the wide applications in a rapidly increasing number of different fields, 3D shape
recognition has become a hot topic in the computer vision field. Many approaches have …

Point cloud upsampling is a basic low-level task, that is important for improving the quality of
a point cloud. However, existing point cloud upsampling methods perform poorly on sparse …

Abstract 3D shape recognition has drawn much attention in recent years. The view-based
approach performs best of all. However, the current multi-view methods are almost all fully …