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

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval
and computer vision research. In this survey, we give a comprehensive overview and key …

Large-scale pre-trained models have shown promising open-world performance for both
vision and language tasks. However, their transferred capacity on 3D point clouds is still …

Nowadays, Deep Neural Networks are among the main tools used in various sciences.
Convolutional Neural Network is a special type of DNN consisting of several convolution …

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and
few-shot image classification are defined on sets of instances. Since solutions to such …

In this work, we present Point Transformer, a deep neural network that operates directly on
unordered and unstructured point sets. We design Point Transformer to extract local and …

We study the problem of designing models for machine learning tasks defined on sets. In
contrast to the traditional approach of operating on fixed dimensional vectors, we consider …

We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape
analysis. Built upon the octree representation of 3D shapes, our method takes the average …

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 …

Deep learning with 3D data such as reconstructed point clouds and CAD models has
received great research interests recently. However, the capability of using point clouds with …