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An accurate method for 3D object reconstruction from unordered sparse views

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Abstract

3D object reconstruction from depth scans has been long investigated problem, but complete solutions are still missing. Recent advances of consumer depth sensors resulted in a number of excellent methods for 3D object reconstruction, with the most impressive results obtained by KinectFusion and Visual Odometry. Both methods reconstruct an object from a dense set of RGB-D video frames, with the important restrictions that the object has to be static and camera transitions has to be smooth during the reconstruction. As an alternative, we propose a method which is more accurate, while being able to reconstruct the object from a sparse set of unordered depth scans. Our method starts by pairwise view matching which relies on robust point pair features. It is followed by the efficient graph-based view consistency check to produce an initial 3D model reconstruction. To improve the accuracy, we perform a simultaneous optimization of the camera positions and the object model, using depth and color information. A quantitative evaluation demonstrates that we can reconstruct objects with equal or better accuracy than the KinectFusion and Visual Odometry, but using significantly lower number of unordered input scans.

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Authors and Affiliations

  1. Techniche Universitat Muenchen, Boltzman Str. 3, Garching, Germany

    Danilo Djordjevic

  2. Faculty of Electronic Engineering, University of Nis, Aleksandra Medvedeva 14, Nis, Serbia

    Stevica Cvetković & Saša V. Nikolić

Authors
  1. Danilo Djordjevic
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  2. Stevica Cvetković
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  3. Saša V. Nikolić
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Correspondence to Danilo Djordjevic.

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Djordjevic, D., Cvetković, S. & Nikolić, S.V. An accurate method for 3D object reconstruction from unordered sparse views. SIViP 11, 1147–1154 (2017). https://doi.org/10.1007/s11760-017-1069-8

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  • DOI: https://doi.org/10.1007/s11760-017-1069-8

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