Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 367-372, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
12 Sep 2017
K. Khoshelham1, L. Díaz Vilariño2,3, M. Peter4, Z. Kang5, and D. Acharya1 1Dept. of Infrastructure Engineering, The University of Melbourne, Parkville 3010 Australia
2Applied Geotechnologies Group, Dept. of Natural Resources and Environmental Engineering, University of Vigo, Spain
3GIS Technology, OTB Research Institute for the Built Environment, Delft University of Technology, Julianalaan 134, Delft, the Netherlands
4Dept. of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, the Netherlands
5Dept. of Remote Sensing and Geo-Information Engineering, School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Keywords: 3D modelling, Point cloud, BIM, Quality, Accuracy, Evaluation, Performance, Automation, Indoor navigation, Geometric reconstruction, Semantics Abstract. Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at:
Conference paper (PDF, 1538 KB)

Citation: Khoshelham, K., Díaz Vilariño, L., Peter, M., Kang, Z., and Acharya, D.: THE ISPRS BENCHMARK ON INDOOR MODELLING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 367-372,, 2017.

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