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

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 …

Multi-view projection methods have demonstrated their ability to reach state-of-the-art
performance on 3D shape recognition. Those methods learn different ways to aggregate …

This paper presents SO-Net, a permutation invariant architecture for deep learning with
orderless point clouds. The SO-Net models the spatial distribution of point cloud by building …

View-based approach that recognizes 3D shape through its projected 2D images has
achieved state-of-the-art results for 3D shape recognition. The major challenge for view …

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 …

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 present a convolutional network that is equivariant to rigid body motions. The model
uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and …

We address the problem of 3D rotation equivariance in convolutional neural networks. 3D
rotations have been a challenging nuisance in 3D classification tasks requiring higher …

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 …