#1 Deep Learning for 3D Point Clouds Analysis
This tutorial presents an overview of the recent developments in neural network architecture for the semantic segmentation of 3D point clouds. We will present the different competing paradigm making up the state-of-the-art of this fast-evolving field, such as: image-based, voxel-based, convolution-based, set-based, and graph-based methods. We then present some strategies for scaling up the algorithms to the scale typical of remote-sensing applications, such as the SuperPoint Graph approach. Duration: all day
The lecture will be completed by a practical exercise in which the participant will follow a guided implementation of the PointNet network for semantic segmentation of aerial point clouds.