TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS
Abderrazzaq Kharroubi|Line Van Wersch|Roland Billen|Florent Poux
Abstract. 3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis semantic assignment are necessary to complement the geometric information. Automated processes that can speed up task highly sought after, especially new supervised approaches. However, availability labelled data training learning models a significant constraint. This paper introduces Tesserae3D, benchmark dataset evaluating machine models, applied segmentation. It publicly available, very high density coloured dataset, accompanied standard multi-class segmentation baseline. consists about 502 million points contains 11 classes covering wide range types. We propose baseline building on radiometric covariance features fed ensemble methods. The results delineate an achievable 89% F1-score made available under https://github.com/akharroubi/Tesserae3D, providing simple interface improve score based feedback from research community.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences|ORBi (University of Liège)