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Results of the ISPRS benchmark on indoor modelling

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.

Ketersediaan
09621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
13 hlm PDF, 4.462 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
Indoor modelling
Reconstruction
Point Cloud
Building Information Model
Scan-to-BIM
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Results of the ISPRS benchmark on indoor modelling
    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.
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