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 …

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 …

We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …

Standard convolution is inherently limited for semantic segmentation of point cloud due to its
isotropy about features. It neglects the structure of an object, results in poor object …

Convolution on 3D point clouds that generalized from 2D grid-like domains is widely
researched yet far from perfect. The standard convolution characterises feature …

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

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 …

Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …

We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance
segmentation in commodity RGB-D scans. The core idea of our method to jointly learn from …

We present an approach to semantic scene analysis using deep convolutional networks.
Our approach is based on tangent convolutions-a new construction for convolutional …