Methods for quality improvement of multibeam and LiDAR point cloud data in the context of 3D surface reconstruction

• Autorzy: Kulawiak M. , Łubniewski Z.
• Języki publikacji: EN

Abstrakty

EN

Point cloud dataset is the transitional data model used in several marine and land remote-sensing applications. During further steps of processing, the transformation of point cloud spatial data to more complex models containing higher order geometric structures like edges and facets may be possible, if an appropriate quality level of input data is provided. Point cloud datasets usually contain a considerable amount of undesirable irregularities, such as strong variability of local point density, missing data, overlapping points and noise caused by scattering characteristics of the environment. For these reasons, processing such data can be quite problematic, especially in the field of object detection and threedimensional surface reconstruction. This paper is focused on applying the proposed methods for reducing the mentioned irregularities from several datasets containing 3D point clouds acquired by multibeam sonars and LiDAR scanners. The article also presents the results obtained by each method, and discusses their advantages.

Słowa kluczowe

EN

multibeam sonar; LiDAR; 3D; point cloud; shape reconstruction; noise reduction

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2016
• Tom: Vol. 19
• Strony: 251--258
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Kulawiak M.

• Gdansk University of Technology Faculty of Electronics, Telecommunications and Informatics Department of Geoinformatics Gdansk, Narutowicza 11/12, Poland

autor

Łubniewski Z.

• Gdansk University of Technology Faculty of Electronics, Telecommunications and Informatics Department of Geoinformatics Gdansk, Narutowicza 11/12, Poland

Bibliografia

• [1] M. Kazhdan, M. Bolitho, H. Hoppe, “Poisson Surface Reconstruction”, Eurographics Symposium on Geometry Processing, 61-70, 2006.
• [2] F. Bernardini, J. Mittleman, H. Ftushmeier, C. Silva, G. Taubin, “The Ball-Pivoting Algorithm for Surface Reconstruction”, IEEE Transactions on Visualization and Computer Graphics, Vol. 5, No. 4, 349-359, 1999.
• [3] N. Amenta, S. Choi, R. K. Kolluri, “The Power Crust”, Proceedings of the sixth ACM symposium on solid modeling and applications, 249-266, 2001.
• [4] V. J. D. Tsai, “Delaunay triangulations in TIN creation: an overview and a linear-time algorithm”, International Journal of Geographical Information Systems, vol. 7 iss. 6, 501-524, 1993, DOI 10.1080/02693799308901979.
• [5] M. Kulawiak, Z. Łubniewski, 3D imaging of underwater objects using multibeam data, Hydroacoustics, Vol. 17, 123-128, 2014.
• [6] H. Benhabiles, O. Aubreton, H. Barki, H. Tabia, “Fast simplification with sharp feature preserving for 3D point clouds”, Programming and Systems (ISPS), 47-52, 2013, DOI 10.1109/ISPS.2013.6581492.
• [7] W. Huang, Y. Li, P. Wen, “Algorithm for 3D point cloud denoising”, Third International Conference on Genetic and Evolutionary Computing, 574-577, 2009, DOI 10.1109/WGEC.2009.139.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)