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Tanks and temples: Benchmarking large-scale scene reconstruction

DC Field Value Language
dc.contributor.authorKnapitsch, Arno-
dc.contributor.authorPark, Jaesik-
dc.contributor.authorZhou, Qian-Yi-
dc.contributor.authorKoltun, Vladlen-
dc.date.accessioned2024-05-09T04:13:49Z-
dc.date.available2024-05-09T04:13:49Z-
dc.date.created2024-05-09-
dc.date.created2024-05-09-
dc.date.issued2017-07-
dc.identifier.citationACM Transactions on Graphics, Vol.36 No.4-
dc.identifier.issn0730-0301-
dc.identifier.urihttps://hdl.handle.net/10371/201317-
dc.description.abstractWe present a benchmark for image-based 3D reconstruction. The benchmark sequences were acquired outside the lab, in realistic conditions. Ground-truth data was captured using an industrial laser scanner. The benchmark includes both outdoor scenes and indoor environments. High-resolution video sequences are provided as input, supporting the development of novel pipelines that take advantage of video input to increase reconstruction fidelity. We report the performance of many image-based 3D reconstruction pipelines on the new benchmark. The results point to exciting challenges and opportunities for future work.-
dc.language영어-
dc.publisherAssociation for Computing Machinary, Inc.-
dc.titleTanks and temples: Benchmarking large-scale scene reconstruction-
dc.typeArticle-
dc.identifier.doi10.1145/3072959.3073599-
dc.citation.journaltitleACM Transactions on Graphics-
dc.identifier.wosid000406432100046-
dc.identifier.scopusid2-s2.0-85030787895-
dc.citation.number4-
dc.citation.volume36-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Jaesik-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.subject.keywordAuthorStructure from motion-
dc.subject.keywordAuthormulti-view stereo-
dc.subject.keywordAuthorimage-based reconstruction-
dc.subject.keywordAuthorlarge-scale scene reconstruction-
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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