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Exploring local regularities for 3D object recognition

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Abstract

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

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Author information

Authors and Affiliations

  1. College of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Huaiwen Tian

  2. Department of Design, Northumbria University, Newcastle upon Type, NE1 8ST, UK

    Shengfeng Qin

Authors
  1. Huaiwen Tian
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  2. Shengfeng Qin
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Corresponding author

Correspondence to Shengfeng Qin.

Additional information

TIAN Huaiwen, born in 1965, is a professor at School of Mechanical Engineering, Southwest Jiaotong University, China. His main research interests include computer graphics, CAD, and reverse engineering.

QIN Shengfeng, born in 1962, is a professor at School of Design, Northumbria University, UK. His main reserach interests include sketch-based interface and modeling, digital design and manufacturing tools.

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Tian, H., Qin, S. Exploring local regularities for 3D object recognition. Chin. J. Mech. Eng. 29, 1104–1113 (2016). https://doi.org/10.3901/CJME.2016.0721.085

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