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Scale invariant point feature (SIPF) for 3D point clouds and 3D multi-scale object detection

  • Neural Computing in Next Generation Virtual Reality Technology
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Abstract

3D point clouds are important for the reconstruction of environment. However, comparing to the artificial VR scene representation methods, 3D point clouds are more difficult to correspond to real scenes. In this paper, a method for detecting keypoints and describing scale invariant point feature of 3D point clouds is proposed. To detect, we first select keypoints as the saliency points with fast changing speed along with all principal directions of the searching area of the point cloud. The searching area is a searching keyscale which represents the unique scale size of the point cloud. Then, the descriptor is encoded based on the shape of a border or silhouette of an object to be detected or recognized. We also introduce a vote-casting-based 3D multi-scale object detection method. Experimental results based on synthetic data, real data and vote-casting scheme show that we can easily deal with the different tasks without additional information.

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Acknowledgments

This work was supported by Liaoning Province Doctoral Research Foundation, 201601315; Dalian Youth Science and Technology Foundation, No. 2015R092; National Natural Science Foundation Granted, No.61300082 and Liaoning Natural Science Foundation, No. 2015020015.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Baowei Lin, Fasheng Wang & Yi Sun

  2. Department of Electronic of Engineering, Dalian Neusoft University of Information, Dalian, China

    Baowei Lin & Fasheng Wang

  3. Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashihiroshima, Japan

    Fangda Zhao

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  1. Baowei Lin
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  2. Fasheng Wang
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  3. Fangda Zhao
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Correspondence to Baowei Lin.

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Lin, B., Wang, F., Zhao, F. et al. Scale invariant point feature (SIPF) for 3D point clouds and 3D multi-scale object detection. Neural Comput & Applic 29, 1209–1224 (2018). https://doi.org/10.1007/s00521-017-2964-1

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  • DOI: https://doi.org/10.1007/s00521-017-2964-1

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