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001447339 001__ 1447339
001447339 003__ OCoLC
001447339 005__ 20230310004113.0
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001447339 008__ 220608s2022\\\\sz\a\\\\ob\\\\001\0\eng\d
001447339 019__ $$a1327550049
001447339 020__ $$a9783030961107$$q(electronic bk.)
001447339 020__ $$a3030961109$$q(electronic bk.)
001447339 020__ $$z9783030961091
001447339 020__ $$z3030961095
001447339 0247_ $$a10.1007/978-3-030-96110-7$$2doi
001447339 035__ $$aSP(OCoLC)1327692704
001447339 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCF$$dOCLCQ
001447339 049__ $$aISEA
001447339 050_4 $$aTK5102.9
001447339 08204 $$a621.382/2$$223/eng/20220608
001447339 1001_ $$aZhang, Guoxiang,$$eauthor.
001447339 24510 $$aTowards optimal point cloud processing for 3D reconstruction /$$cGuoxiang Zhang, YangQuan Chen.
001447339 264_1 $$aCham :$$bSpringer,$$c[2022]
001447339 264_4 $$c©2022
001447339 300__ $$a1 online resource (xix, 87 pages) :$$billustrations (chiefly color).
001447339 336__ $$atext$$btxt$$2rdacontent
001447339 337__ $$acomputer$$bc$$2rdamedia
001447339 338__ $$aonline resource$$bcr$$2rdacarrier
001447339 4901_ $$aSpringerBriefs in electrical and computer engineering. SpringerBriefs in signal processing,$$x2196-4084
001447339 504__ $$aIncludes bibliographical references and index.
001447339 5050_ $$a1. Introduction -- 2. Preliminaries -- 3. Fractional-Order Random Sample Consensus -- 4. Online Sifting of Loop Detections for 3D Reconstruction of Caves -- 5. Dense Map Posterior: A Novel Quality Metric for 3D Reconstruction -- 6. Offline Sifting and Majorization of Loop Detections -- 7. Conclusion and Future Opportunities -- Appendix: More Information on Results Reproducibility.
001447339 506__ $$aAccess limited to authorized users.
001447339 520__ $$aThis SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods. The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data. The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.
001447339 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 8, 2022).
001447339 650_0 $$aSignal processing$$xMathematical models.
001447339 650_0 $$aSignal detection.
001447339 650_0 $$aCloud computing.
001447339 650_0 $$aThree-dimensional imaging.
001447339 655_0 $$aElectronic books.
001447339 7001_ $$aChen, YangQuan,$$d1966-$$eauthor.
001447339 77608 $$iPrint version: $$z3030961095$$z9783030961091$$w(OCoLC)1291392108
001447339 830_0 $$aSpringerBriefs in electrical and computer engineering.$$pSignal processing.$$x2196-4084
001447339 852__ $$bebk
001447339 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-96110-7$$zOnline Access$$91397441.1
001447339 909CO $$ooai:library.usi.edu:1447339$$pGLOBAL_SET
001447339 980__ $$aBIB
001447339 980__ $$aEBOOK
001447339 982__ $$aEbook
001447339 983__ $$aOnline
001447339 994__ $$a92$$bISE