Results of the ISPRS benchmark on indoor modelling

Abstract

This paper reports the results of the ISPRS benchmark on indoor modelling. Reconstructed models submitted by 11 participating teams are evaluated on a dataset comprising 6 point clouds representing indoor environments of different complexity. The evaluation is based on measuring the completeness, correctness, and accuracy of the reconstructed wall elements through comparison with manually generated reference models. The results show that the performance of the methods varies across different datasets, but generally the reconstruction methods achieve better results for the point clouds with higher accuracy and density and fewer gaps, as well as the point clouds representing less complex environments. Filtering clutter points in a pre-processing step contributes to higher correctness, and making strong assumptions on the shape of the reconstructed elements contributes to higher completeness and accuracy for models of Manhattan World environments.