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Open AccessArticle

Multisource Point Clouds, Point Simplification and Surface Reconstruction

1
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Masala, Finland
2
Department of Built Environment, School of Engineering, Aalto University, 02150 Espoo, Finland
3
Department of Geography and Geology, University of Turku, 20014 Turun, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2659; https://doi.org/10.3390/rs11222659
Received: 15 October 2019 / Revised: 11 November 2019 / Accepted: 12 November 2019 / Published: 13 November 2019
(This article belongs to the Section Engineering Remote Sensing)
As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that (i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; (ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; (iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; (iv) all reconstruction methods are beneficial from the reduction of running time; and (v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics. View Full-Text
Keywords: point simplification; surface reconstruction; Alpha shapes; screened Poisson reconstruction; APSS; Crust point simplification; surface reconstruction; Alpha shapes; screened Poisson reconstruction; APSS; Crust
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MDPI and ACS Style

Zhu, L.; Kukko, A.; Virtanen, J.-P.; Hyyppä, J.; Kaartinen, H.; Hyyppä, H.; Turppa, T. Multisource Point Clouds, Point Simplification and Surface Reconstruction. Remote Sens. 2019, 11, 2659. https://doi.org/10.3390/rs11222659

AMA Style

Zhu L, Kukko A, Virtanen J-P, Hyyppä J, Kaartinen H, Hyyppä H, Turppa T. Multisource Point Clouds, Point Simplification and Surface Reconstruction. Remote Sensing. 2019; 11(22):2659. https://doi.org/10.3390/rs11222659

Chicago/Turabian Style

Zhu, Lingli; Kukko, Antero; Virtanen, Juho-Pekka; Hyyppä, Juha; Kaartinen, Harri; Hyyppä, Hannu; Turppa, Tuomas. 2019. "Multisource Point Clouds, Point Simplification and Surface Reconstruction" Remote Sens. 11, no. 22: 2659. https://doi.org/10.3390/rs11222659

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