A Benchmark for large-scale heritage point cloud segmentation
The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins as well as facilitate restoration and conservation works.
Our proposed dataset, named ArCH – Architectural Cultural Heritage is composed of 17 annotated scenes, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. Other 10 point clouds not labeled yet are available and you are very welcome to help us in this work!
The realised benchmark originates from the collaboration of different universities and research institutes (Politecnico di Torino, Università Politecnica delle Marche, FBK Trento, Italy, and INSA Strasbourg, France). It is unique as it offers, for the first time to the research community, annotated point clouds describing heritage scenes. These point clouds, labelled with 10 classes, are meant to facilitate the development, training, testing and evaluation of machine learning algorithms as well as its subset of deep learning methods in the heritage field.
For a more profitable use of this benchmark, aside from free download of all data, we provide public results of the submitted approaches, providing rankings about the most performing ones.
When using this dataset in your research, we will be happy if you cite:
Reference paper to be cited:
Matrone, F., Lingua, A., Pierdicca, R., Malinverni, E. S., Paolanti, M., Grilli, E., Remondino, F., Murtiyoso, A., and Landes, T.: A BENCHMARK FOR LARGE-SCALE HERITAGE POINT CLOUD SEMANTIC SEGMENTATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1419–1426, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1419-2020, 2020.
All datasets and benchmarks on this page are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license.