Inference-Based Surface Reconstruction of Cluttered Environments

Handle URI:
http://hdl.handle.net/10754/598619
Title:
Inference-Based Surface Reconstruction of Cluttered Environments
Authors:
Biggers, K.; Keyser, J.
Abstract:
We present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion. © 2012 IEEE.
Citation:
Biggers K, Keyser J (2012) Inference-Based Surface Reconstruction of Cluttered Environments. IEEE Transactions on Visualization and Computer Graphics 18: 1255–1267. Available: http://dx.doi.org/10.1109/TVCG.2011.263.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Visualization and Computer Graphics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Aug-2012
DOI:
10.1109/TVCG.2011.263
PubMed ID:
21968935
Type:
Article
ISSN:
1077-2626
Sponsors:
This work was supported in part by US National ScienceFoundation (NSF) Grant IIS-0917286 and by AwardNo. KUS-C1-016-04 from King Abdullah University ofScience and Technology. The authors would like to thankAnn McNamara for the use of her scanner and laboratory,and the Stanford 3D Scanning Repository for the Bunnymodel used in our figures.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorBiggers, K.en
dc.contributor.authorKeyser, J.en
dc.date.accessioned2016-02-25T13:33:15Zen
dc.date.available2016-02-25T13:33:15Zen
dc.date.issued2012-08en
dc.identifier.citationBiggers K, Keyser J (2012) Inference-Based Surface Reconstruction of Cluttered Environments. IEEE Transactions on Visualization and Computer Graphics 18: 1255–1267. Available: http://dx.doi.org/10.1109/TVCG.2011.263.en
dc.identifier.issn1077-2626en
dc.identifier.pmid21968935en
dc.identifier.doi10.1109/TVCG.2011.263en
dc.identifier.urihttp://hdl.handle.net/10754/598619en
dc.description.abstractWe present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion. © 2012 IEEE.en
dc.description.sponsorshipThis work was supported in part by US National ScienceFoundation (NSF) Grant IIS-0917286 and by AwardNo. KUS-C1-016-04 from King Abdullah University ofScience and Technology. The authors would like to thankAnn McNamara for the use of her scanner and laboratory,and the Stanford 3D Scanning Repository for the Bunnymodel used in our figures.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectobject recognitionen
dc.subjectsegmentationen
dc.subjectsurface fittingen
dc.subjectThree-dimensional/stereo scene analysisen
dc.titleInference-Based Surface Reconstruction of Cluttered Environmentsen
dc.typeArticleen
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en

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