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    • 36-Issue 7
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    Saliency-aware Real-time Volumetric Fusion for Object Reconstruction

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    Date
    2016
    Author
    Yang, Sheng
    Chen, Kang
    Liu, Minghua
    Fu, Hongbo
    Hu, Shi-Min ORCID
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    Abstract
    We present a real-time approach for acquiring 3D objects with high fidelity using hand-held consumer-level RGB-D scanning devices. Existing real-time reconstruction methods typically do not take the point of interest into account, and thus might fail to produce clean reconstruction results of desired objects due to distracting objects or backgrounds. In addition, any changes in background during scanning, which can often occur in real scenarios, can easily break up the whole reconstruction process. To address these issues, we incorporate visual saliency into a traditional real-time volumetric fusion pipeline. Salient regions detected from RGB-D frames suggest user-intended objects, and by understanding user intentions our approach can put more emphasis on important targets, and meanwhile, eliminate disturbance of non-important objects. Experimental results on realworld scans demonstrate that our system is capable of effectively acquiring geometric information of salient objects in cluttered real-world scenes, even if the backgrounds are changing.
    BibTeX
    @article {10.1111:cgf.13282,
    journal = {Computer Graphics Forum},
    title = {{Saliency-aware Real-time Volumetric Fusion for Object Reconstruction}},
    author = {Yang, Sheng and Chen, Kang and Liu, Minghua and Fu, Hongbo and Hu, Shi-Min},
    year = {2016},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13282}
    }
    URI
    http://dx.doi.org/10.1111/cgf.13282
    https://diglib.eg.org:443/handle/10.1111/cgf13282
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    • 36-Issue 7

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