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Photometric Stereopsis for 3D Reconstruction of Space Objects

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Handbook of Dynamic Data Driven Applications Systems

Abstract

The use of photometric stereopsis approaches to estimate the geometry of a resident space object (RSO) from image data is detailed. The set of algorithms and methods for shape estimation form an integral element of a Dynamic Data Driven Application System (DDDAS) for enhancing space situational awareness, where, sensor tasking and scheduling operations are carried out based upon the RSO orbital and geometric attributes, as estimated from terrestrial and space-based sensor systems. Techniques for estimating the relative motion between successive frames using image features are used for data alignment before surface normal estimation. Mathematical models of photometry and imaging physics are exploited to infer the surface normals from images of the target object under varied illumination conditions. Synthetic images generated from physics based ray-tracing engine are used to demonstrate the utility of the proposed algorithms.The proposed framework results in a estimates of the surface shape of the target object, which can subsequently used in forward models for prediction, data assimilation and subsequent sensor tasking operations. Sensitivity analysis is used to quantify the uncertainty of reconstructed surface.

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Acknowledgements

This work is based upon work supported by the AFOSR grant FA9550-15-1-0313. Drs. Erik Blasch, Sai Ravella and Frederica Darema are acknowledged for the technical discussions. The authors are also grateful to the inputs of the anonymous reviewers. Their inputs enhanced the quality of the chapter extensively.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA

    Xue Iuan Wong & Puneet Singla

  2. Aerospace Engineering, Director, Land, Air and Space Robotics Laboratory, Texas A&M University, College Station, TX, USA

    Manoranjan Majji

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  1. Xue Iuan Wong
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  3. Puneet Singla
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Correspondence to Xue Iuan Wong .

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Editors and Affiliations

  1. Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA

    Erik Blasch

  2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  3. Information Directorate, Air Force Research Laboratory, Rome, NY, USA

    Alex Aved

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Wong, X.I., Majji, M., Singla, P. (2018). Photometric Stereopsis for 3D Reconstruction of Space Objects. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-95504-9_13

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-95504-9

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