Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

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

Pre-training across 3D vision and language remains under development because of limited
training data. Recent works attempt to transfer vision-language (VL) pre-training methods to …

We present a network architecture for processing point clouds that directly operates on a
collection of points represented as a sparse set of samples in a high-dimensional lattice …

Point clouds acquired through laser scanning and stereo vision techniques have been
applied in a wide range of applications, proving to be optimal sources for mapping 3D urban …

We study the problem of 3D object generation. We propose a novel framework, namely 3D
Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic …

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage,
and hydrocephalus are critical to achieving positive outcomes and preserving neurologic …

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative
power of deep learning models with softmax loss for the classification of 3D data, while …

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …