Acquiring 3D indoor environments with variability and repetition

Handle URI:
http://hdl.handle.net/10754/575789
Title:
Acquiring 3D indoor environments with variability and repetition
Authors:
Kim, Youngmin; Mitra, Niloy J. ( 0000-0002-2597-0914 ) ; Yan, Dongming ( 0000-0003-2209-2404 ) ; Guibas, Leonidas J.
Abstract:
Large-scale acquisition of exterior urban environments is by now a well-established technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically contain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or non-regular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a low-end handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen objects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings. © 2012 ACM.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Visual Computing Center (VCC)
Publisher:
Association for Computing Machinery (ACM)
Journal:
ACM Transactions on Graphics
Conference/Event name:
Proceedings of ACM SIGGRAPH Asia 2012
Issue Date:
1-Nov-2012
DOI:
10.1145/2366145.2366157
Type:
Conference Paper
ISSN:
07300301
Sponsors:
We acknowledge the support of a gift from Qualcomm Corporation, the Max Planck Center for Visual Computing and Communications, NSF grants 0914833 and 1011228, a KAUST AEA grant, and Marie Curie Career Integration Grant 303541.
Appears in Collections:
Conference Papers; Visual Computing Center (VCC); Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKim, Youngminen
dc.contributor.authorMitra, Niloy J.en
dc.contributor.authorYan, Dongmingen
dc.contributor.authorGuibas, Leonidas J.en
dc.date.accessioned2015-08-24T09:26:15Zen
dc.date.available2015-08-24T09:26:15Zen
dc.date.issued2012-11-01en
dc.identifier.issn07300301en
dc.identifier.doi10.1145/2366145.2366157en
dc.identifier.urihttp://hdl.handle.net/10754/575789en
dc.description.abstractLarge-scale acquisition of exterior urban environments is by now a well-established technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically contain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or non-regular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a low-end handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen objects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings. © 2012 ACM.en
dc.description.sponsorshipWe acknowledge the support of a gift from Qualcomm Corporation, the Max Planck Center for Visual Computing and Communications, NSF grants 0914833 and 1011228, a KAUST AEA grant, and Marie Curie Career Integration Grant 303541.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectAcquisitionen
dc.subjectRealtime Modelingen
dc.subjectScene understandingen
dc.subjectShape analysisen
dc.titleAcquiring 3D indoor environments with variability and repetitionen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.identifier.journalACM Transactions on Graphicsen
dc.conference.date2 – 5 November 2015en
dc.conference.nameProceedings of ACM SIGGRAPH Asia 2012en
dc.conference.locationKobe, Japanen
dc.contributor.institutionStanford University, United Statesen
dc.contributor.institutionUniv. College London, United Kingdomen
kaust.authorMitra, Niloy J.en
kaust.authorYan, Dongmingen
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