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Superpixel-Guided Two-View Deterministic Geometric Model Fitting

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Abstract

Geometric model fitting is a fundamental research topic in computer vision and it aims to fit and segment multiple-structure data. In this paper, we propose a novel superpixel-guided two-view geometric model fitting method (called SDF), which can obtain reliable and consistent results for real images. Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm. The proposed deterministic sampling algorithm generates a set of initial model hypotheses according to the prior information of superpixels. Then the proposed updating strategy further improves the quality of model hypotheses. After that, by analyzing the properties of the updated model hypotheses, the proposed model selection algorithm extends the conventional “fit-and-remove” framework to estimate model instances in multiple-structure data. The three parts are tightly coupled to boost the performance of SDF in both speed and accuracy, and SDF has the deterministic nature. Experimental results show that the proposed SDF has significant advantages over several state-of-the-art fitting methods when it is applied to real images with single-structure and multiple-structure data.

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Notes

  1. We download the codes from their websites.

  2. http://www.cse.usf.edu/~sarkar/BLOGS/.

  3. http://www.robots.ox.ac.uk/~vgg/data/.

  4. http://cs.adelaide.edu.au/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants U1605252, 61702431, 61472334 and 61571379, by the China Postdoctoral Science Foundation 2017M620272 and by the Fujian Province Education-Science Project for Middle-aged and Young Teachers JAT170024. This work was carried out when David Suter was with The University of Adelaide. This work was partially supported by ARC Grant DP130102524.

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Authors and Affiliations

  1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, China

    Guobao Xiao, Hanzi Wang & Yan Yan

  2. School of Science, Edith Cowan University, Joondalup, Australia

    David Suter

Authors
  1. Guobao Xiao
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  2. Hanzi Wang
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  3. Yan Yan
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  4. David Suter
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Corresponding author

Correspondence to Hanzi Wang.

Additional information

Communicated by J. Kosecka.

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Xiao, G., Wang, H., Yan, Y. et al. Superpixel-Guided Two-View Deterministic Geometric Model Fitting. Int J Comput Vis 127, 323–339 (2019). https://doi.org/10.1007/s11263-018-1100-8

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