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Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference.

Li Zheng1, Zhukun Li1

  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.

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|August 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel point cloud fusion method using Fast Point Feature Histograms (FPFH) to improve registration accuracy for multi-source point cloud data, especially with noise and low precision. The approach enhances accuracy and robustness in point cloud registration.

Keywords:
fast point feature histogramsiterative closest pointpoint cloud registrationvirtual namesake point

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Area of Science:

  • Computer Vision and Robotics
  • Geospatial Data Science
  • 3D Reconstruction and Modeling

Background:

  • Multi-source point cloud data from various sensors (e.g., aerial imagery, LiDAR, terrestrial scanning) have diverse accuracy, range, and magnitude characteristics.
  • Existing point cloud fusion methods aim to leverage the strengths of different data sources for enhanced accuracy.
  • The Iterative Closest Point (ICP) algorithm is a foundational method for point cloud registration but can be sensitive to noise and data quality.

Purpose of the Study:

  • To propose a novel virtual namesake point multi-source point cloud data fusion method based on Fast Point Feature Histograms (FPFH) feature difference.
  • To improve the registration accuracy and robustness of point cloud fusion, particularly for data with noise, varying resolutions, and local distortions.
  • To enhance the detailed matching of low-precision point clouds to a target point cloud.

Main Methods:

  • Utilizes the Fast Point Feature Histograms (FPFH) feature difference to identify corresponding point pairs within the Iterative Closest Point (ICP) framework.
  • Generates virtual namesake points by using a convolutional neural network (CNN) based on the F2 distance of FPFH features between source and target point clouds within established voxels.
  • Employs a CNN to output virtual corresponding points, improving registration realism and theoretical accuracy for multi-source point cloud data.

Main Results:

  • Achieves accuracy equivalent to the best existing algorithms on clean point clouds and point clouds with different resolutions.
  • Demonstrates superior performance compared to other algorithms when dealing with noisy and distorted point cloud data.
  • Effectively matches low-precision point clouds to target point clouds in detail, exhibiting enhanced stability and robustness.

Conclusions:

  • The proposed virtual namesake point fusion method offers a more reasonable and accurate approach to multi-source point cloud registration than traditional ICP methods.
  • The FPFH feature difference combined with CNN-generated virtual points provides strong robustness against noise and local distortions.
  • This method significantly improves the detailed accuracy and stability of registering low-precision point clouds, making it valuable for diverse 3D data fusion applications.