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Iterative Closest Point with Minimal Free Space Constraints

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

The Iterative Closest Point (ICP) method is widely used for fitting geometric models to sensor data. By formulating the problem as a minimization of distances evaluated at observed surface points, the method is computationally efficient and applicable to a rich variety of model representations. However, when the scene surface is only partially visible, the model can be ill-constrained by surface observations alone. Existing methods that penalize free space violations may resolve this issue, but require that the explicit model surface is available or can be computed quickly, to remain efficient. We introduce an extension of ICP that integrates free space constraints, while the number of distance computations remains linear in the scene’s surface area. We support arbitrary shape spaces, requiring only that the distance to the model surface can be computed at a given point. We describe an implementation for range images and validate our method on implicit model fitting problems that benefit from the use of free space constraints.

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Acknowledgments

This work is partly supported by the Research Council of Norway through the Centre of Excellence funding scheme, project number 223254, NTNU AMOS.

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Authors and Affiliations

  1. Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway

    Simen Haugo & Annette Stahl

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  1. Simen Haugo
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  2. Annette Stahl
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Corresponding author

Correspondence to Simen Haugo .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Haugo, S., Stahl, A. (2020). Iterative Closest Point with Minimal Free Space Constraints. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_7

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