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An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and

Fei Wang1, Zhendong Liu2,3, Hongchun Zhu1

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a new method for selecting feature points in structure from motion (SfM) 3D reconstruction. The improved technique enhances matching accuracy and aerial triangulation by considering semantic and structural image characteristics.

Keywords:
3D reconstructionfeature point selectionimage semantic and structural characteristicsoblique imagestwo-tuple classification model

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Area of Science:

  • Photogrammetry
  • Computer Vision
  • Geospatial Information Science

Background:

  • Feature matching is critical for 3D reconstruction using structure from motion (SfM), but it's time-consuming and affects accuracy.
  • Current methods often lack consideration for feature point stability and spatial distribution, leading to suboptimal results in SfM.

Purpose of the Study:

  • To propose an improved method for stable feature point selection in SfM that incorporates image semantic and structural characteristics.
  • To enhance the efficiency and accuracy of 3D reconstruction from oblique images.

Main Methods:

  • Utilized visible-band difference vegetation index for vegetation area identification and an optimized line segment detector for line feature extraction.
  • Developed a feature point classification model using vegetation and line features as semantic and structural constraints, categorizing points into three types.
  • Implemented a progressive selection algorithm to choose feature points based on their class and level within Difference-of-Gaussian (DoG) pyramids.

Main Results:

  • The proposed method effectively reduces the number of selected feature points while better reflecting scene structures.
  • Demonstrated a 20% decrease in average aerial triangulation reprojection error and a 3% increase in feature point matching rate.
  • Validated the selection of more stable and reasonable feature points compared to state-of-the-art methods.

Conclusions:

  • The novel approach improves feature point selection for SfM by integrating semantic and structural information.
  • This leads to more accurate and robust 3D reconstruction from oblique imagery.
  • The method offers a significant advancement in SfM techniques for applications requiring precise 3D modeling.