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_c383914 _d383914 |
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| 001 | 383914 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230112000747.0 | ||
| 006 | a||||fo|||| 00| 0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230111s2022 sz | s |||| 0|eng d | ||
| 020 | _a9783030961107 | ||
| 024 | 7 |
_a10.1007/978-3-030-96110-7 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
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| 050 | 4 |
_aTK5102.9 _b2022 EB |
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| 100 | 1 |
_aZhang, Guoxiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685934 |
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| 245 | 1 | 0 |
_aTowards Optimal Point Cloud Processing for 3D Reconstruction _cby Guoxiang Zhang, YangQuan Chen |
| 250 | _a1st edition 2022 | ||
| 264 | 1 |
_aCham _bSpringer International Publishing _c2022 |
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| 300 |
_a1 recurso en línea (XIX, 87 páginas) _b29 ilustraciones, 25 ilustraciones a color |
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| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 347 |
_aarchivo de texto _bPDF |
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| 490 | 1 |
_aSpringerBriefs in Signal Processing _x2196-4084 |
|
| 505 | 0 | _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. | |
| 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. | ||
| 988 | _aSpringer_Engineering_2022 | ||
| 650 | 7 |
_2embne _9150608 _aProceso de señales |
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| 650 | 7 |
_2embne _9666069 _aInformática en la nube |
|
| 700 | 1 |
_aChen, YangQuan, _eautor _0(orcid)0000-0002-7422-5988 _1https://orcid.org/0000-0002-7422-5988 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685935 _d1966- |
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| 776 | 0 | 8 |
_iPrinted edition: _z9783030961091 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030961114 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-96110-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b01/2023 _dz _eIG _zSI |
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