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Incremental Solid Modeling from Sparse and Omnidirectional Structure-from-Motion Data

Abstract : This paper introduces a sparse and incremental 2-manifold surface reconstruction method. It uses a sparse 3D point cloud generated by a Structure-from-Motion algorithm (SfM) as its main input as opposed to the more common dense algorithms. Furthermore, our method is incremental: the surface is updated for every new camera pose computed by SfM, and the update occurs in a small neighborhood of the new camera pose. Compared to the other surface reconstruction methods, our method has the advantage to have all these properties at the same time. The quality and execution time of the proposed algorithm is evaluated on a large scale (2.5 km.) real sequence taken in an urban environment, and the method is quantitatively evaluated on a synthetic urban scene.
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Submitted on : Wednesday, November 15, 2017 - 11:06:13 AM
Last modification on : Wednesday, February 24, 2021 - 4:16:01 PM
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  • HAL Id : hal-01635442, version 1


Vadim Litvinov, Maxime Lhuillier. Incremental Solid Modeling from Sparse and Omnidirectional Structure-from-Motion Data. British Machine Vision Conference, Sep 2013, Bristol, United Kingdom. ⟨hal-01635442⟩



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