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Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering

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Advances in Computer Graphics (CGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13443))

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Abstract

The Grid-based Motion Statistics (GMS) is a popular feature matching filtering method, and can effectively support 3D reconstruction systems such as ORB-SLAM and has been used effectively in many fields. However, the GMS divides the image into a certain number of grids with a fixed size, which cannot better reflect the feature information of the region. So that when large affine changes occur in the images, the grids cannot delineate a reasonable consistent region. Such a region division leads to errors and even failures in the subsequent process using the grid to reject error feature matching. As a consequence, this paper proposes an adaptive regional motion statistics method based on adaptive region division for region detection to replace the fixed grid division, which enhances the affine invariance of the feature matching filtering algorithm, and verifies the effectiveness of this paper's method through the precision experiments of feature matching and homography matrix.

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Acknowledgements

This work was supported by the National Natural Science Foun-dation of China [grant numbers 61872291, 62172190], and The Innovation & Entre-preneurship Plan of Jiangsu Province (JSSCRC2021532).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Bin Nan, Yinghui Wang, Yanxing Liang & Pengjiang Qian

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Min Wu

  3. Gastroenterology Department, Affiliated Hospital of Jiangnan University, Wuxi, 214122, China

    Gang Lin

Authors
  1. Bin Nan
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  2. Yinghui Wang
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  3. Yanxing Liang
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  4. Min Wu
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  5. Pengjiang Qian
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  6. Gang Lin
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Corresponding authors

Correspondence to Bin Nan or Yinghui Wang .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth University, Poole, UK

    Jian Zhang

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. University of Crete, Heraklion, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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Nan, B., Wang, Y., Liang, Y., Wu, M., Qian, P., Lin, G. (2022). Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_11

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  • DOI: https://doi.org/10.1007/978-3-031-23473-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

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