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A novel rapid point-cloud surface reconstruction algorithm for laser imaging radar

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Abstract

In order to obtain the fast three-dimensional surface reconstruction from given scattered point clouds, a novel improved point-cloud surface reconstruction algorithm for laser imaging radar is proposed so as to reconstruct the three-dimensional depth surface from the depth data and image data in this paper. Firstly, the three-dimensional space is partitioned into voxels with local distance points and finds outliers with point histogram features; then the Gaussian process (GP) regression is adopted to generate a plane similar to a Gaussian distribution; finally, the high-resolution gray data and three-dimensional interpolation points are fused by using Markov random fields to build a dense three-dimensional depth surface. Experimental results show that our proposed algorithm will greatly improve the robustness and reconstruction accuracy of three-dimensional surface reconstruction algorithm and can be used to assist unmanned driving in complex urban scenes.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Yan’an University, Yan’an, 716000, Shannxi, China

    Wendong Wang

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  1. Wendong Wang
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Correspondence to Wendong Wang.

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Wang, W. A novel rapid point-cloud surface reconstruction algorithm for laser imaging radar. Multimed Tools Appl 78, 8737–8749 (2019). https://doi.org/10.1007/s11042-018-6244-6

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  • DOI: https://doi.org/10.1007/s11042-018-6244-6

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