SCIPoC: Semantic Classification of Indoor Point Cloud

Mels Smit, Xiaoai Li, Zhaiyu Chen, Mihai-Alexandru Erbasu, Yustisi Ardhitasari Lumban Gaol

This research tries to add additional value to point cloud by using deep learning, specifically in the indoor environment. This is done by first doing a neural network comparison followed by a case study. The neural network comparison is based on the training speed, accuracy, ease of use concerning training on external datasets and setting up the network and space efficiency. After the comparison, we chose to continue with the PointCNN network during the case study. The case study is performed on data the NS (Nederlandse Spoorwegen) provided to us and all test results we got from our experiments can be visualized using the web application we developed along with this project. In the case study we found through 4 different experiments that unbalanced data makes for bad results, but when a scene is labelled correctly very good results can be found in a local scene.