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 …

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 …

A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

The development of practical applications, such as autonomous driving and robotics, has
brought increasing attention to 3D point cloud understanding. While deep learning has …

Point cloud is a widely used 3D data form, which can be produced by depth sensors, such
as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and …

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 …

Three dimensional (3D) object recognition is becoming a key desired capability for many
computer vision systems such as autonomous vehicles, service robots and surveillance …

It is a challenge to design a deep neural network for raw point cloud, which is disordered
and unstructured data. In this paper, we introduce a cross self-attention network (CSANet) to …

Deterministic down-sampling of an unordered point cloud in a deep neural network has not
been rigorously studied so far. Existing methods down-sample the points regardless of their …

Abstract 3D point clouds deep learning is a promising field of research that allows a neural
network to learn features of point clouds directly, making it a robust tool for solving 3D scene …