Speaker
Naoya Chiba
(Tohoku University)
Description
Since the use of point cloud data by deep learning has become widespread, research on neural networks for handling point clouds and methods for handling 3D data has been focused in this research area. When data points are not arranged on a grid and are not sequential, they are referred to as "irregular data structures". For such data, including point clouds, graphs, and tabular data, it is difficult to apply existing deep learning methods (such as convolution) for purely regular data structures. In this talk, I will present a deep learning approach for dealing with such irregular data structures, using 3D point clouds as an example.
Primary author
Naoya Chiba
(Tohoku University)
