Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation

Research output: Contribution to journalArticle

Abstract

Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated training data for deep learning. However, there is currently no free specialized software available that can efficiently annotate large 3D point clouds. We fill this gap by introducing PC-Annotate - a public annotation tool for 3D point cloud research. The proposed tool not only enables systematic annotation with a variety of fundamental volumetric shapes, but also provides useful functionalities of point cloud registration and the generation of volumetric samples that can be readily consumed by contemporary deep learning point cloud models. We also introduce a large outdoor public dataset for 3D semantic segmentation. The proposed dataset, PC-Urban is collected in a civic setup with Ouster LiDAR and labeled with PC-Annotate. It has over 4.3 billion points covering 66K frames and 25 annotated classes. Finally, we provide baseline semantic segmentation results on PC-Urban for popular recent techniques.

Original languageEnglish
Article number9363898
Pages (from-to)35984-35996
Number of pages13
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

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