SHREC 2023: Point cloud change detection for city scenes

• Published in: Computers & graphics 2023-10, Vol.115, p.35-42
• Main Authors: Gao, Yang, Yuan, Honglin, Ku, Tao, Veltkamp, Remco C., Zamanakos, Georgios, Tsochatzidis, Lazaros, Amanatiadis, Angelos, Pratikakis, Ioannis, Panou, Aliki, Romanelis, Ioannis, Fotis, Vlassis, Arvanitis, Gerasimos, Moustakas, Konstantinos
• Format: Article
• Language: English
• Subjects: Point cloud change detection; Retrieval models and ranking; SHREC
• ISSN: 0097-8493; 1873-7684
• DOI: 10.1016/j.cag.2023.06.025

Localization and navigation are the two most important tasks for mobile robots, which require an up-to-date and accurate map. However, to detect map changes from crowdsourced data is a challenging task, especially from billions of points collected by 3D acquisition devices. Collecting 3D data often requires expensive data acquisition equipment and there are limited data sources to evaluate point cloud change detection. To address these issues, in this Shape Retrieval Challenge (SHREC) track, we provide a city-scene dataset with real and synthesized data to detect 3D point cloud change. The dataset consists of 866 pairs of object changes from 78 city-scene 3D point clouds collected by LiDAR and 845 pairs of object changes from 100 city-scene 3D point clouds generated by a high-fidelity simulator. We compare three methods on this benchmark. Evaluation results show that data-driven methods are the current trend in 3D point cloud change detection. Besides, the siamese network architecture is helpful to detect changes in our dataset. We hope this benchmark and comparative evaluation results will further enrich and boost the research of point cloud change detection and its applications. [Display omitted] •A representative dataset providing high-quality annotated 3D point clouds, which contains rich urban objects and covers a wide range of change detection challenges.•The dataset fills the vacancy in the point cloud change detection task.•A comprehensive evaluation of point cloud change detection approaches. We organize the Shape Retrieval Challenge (SHREC) benchmark on point cloud change detection.•Different evaluation metrics are used to compare the proposed methods based on our datasets.