3D Semantic Segmentation of Point Cloud Data

With increasing availability and affordability of 3D sensors, such as laser scanners and RGB-D systems in smartphones, 3D scans are becoming the new digital photograph. In the 2D image domain we are already able to perform automatic detection of objects and pixel-wis segmentation into different categories. These tasks are dominated by the utilization of convolutional neural networks.

For this project we will demonstrate how to create high quality 3D scans of indoor environments for visualization tasks, computer games and virtual reality applications. Using these 3D scans, we will then explore methods to analyze and segment the reconstructed geometric data. The goal is to understand and extend technologies that can be used to identify both basic shapes and complex objects of interest such as works of art or museum artifacts.