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 …

Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …

Gesture recognition has attracted considerable attention owing to its great potential in
applications. Although the great progress has been made recently in multi-modal learning …

This article proposes a hybrid multi-dimensional features fusion structure of spatial and
temporal segmentation model for automated thermography defects detection. In addition, the …

Inspired by the great success achieved by CNN in image recognition, view-based methods
applied CNNs to model the projected views for 3D object understanding and achieved …

We present a novel and flexible architecture for point cloud segmentation with dual-
representation iterative learning. In point cloud processing, different representations have …

In point cloud learning, sparsity and geometry are two core properties. Recently, many
approaches have been proposed through single or multiple representations to improve the …

Computational protein-binding studies are widely used to investigate fundamental biological
processes and facilitate the development of modern drugs, vaccines and therapeutics …

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 …

Conventional 3D convolutional neural networks (CNNs) are computationally expensive,
memory intensive, prone to overfitting, and most importantly, there is a need to improve their …