Saliency-aware Real-time Volumetric Fusion for Object Reconstruction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Sheng Yang
  • Kang Chen
  • Minghua Liu
  • Hongbo Fu
  • Shi-Min Hu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)167-174
Journal / PublicationComputer Graphics Forum
Volume36
Issue number7
Publication statusPublished - Oct 2017

Link(s)

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 real-world 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.

Research Area(s)

  • CCS Concepts, Computing methodologies → Reconstruction

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Saliency-aware Real-time Volumetric Fusion for Object Reconstruction. / Yang, Sheng; Chen, Kang; Liu, Minghua; Fu, Hongbo; Hu, Shi-Min.

In: Computer Graphics Forum, Vol. 36, No. 7, 10.2017, p. 167-174.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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