Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

The big empirical success of group equivariant networks has led in recent years to the
sprouting of a great variety of equivariant network architectures. A particular focus has …

Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …

Polygonal meshes provide an efficient representation for 3D shapes. They explicitly
captureboth shape surface and topology, and leverage non-uniformity to represent large flat …

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 …

Learned 3D representations of human faces are useful for computer vision problems such
as 3D face tracking and reconstruction from images, as well as graphics applications such …

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 …