000 | 03607nam a22005415i 4500 | ||
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001 | 978-3-658-30567-3 | ||
003 | DE-He213 | ||
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_a9783658305673 _9978-3-658-30567-3 |
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024 | 7 |
_a10.1007/978-3-658-30567-3 _2doi |
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_aGolyanik, Vladislav. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aRobust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds _h[electronic resource] / _cby Vladislav Golyanik. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aWiesbaden : _bSpringer Fachmedien Wiesbaden : _bImprint: Springer Vieweg, _c2020. |
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300 |
_aXXIV, 352 p. 119 illus., 13 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aScalable Dense Non-rigid Structure from Motion -- Shape Priors in Dense Non-rigid Structure from Motion -- Probabilistic Point Set Registration with Prior Correspondences -- Point Set Registration Relying on Principles of Particle Dynamics. | |
520 | _aVladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book. Contents Scalable Dense Non-rigid Structure from Motion Shape Priors in Dense Non-rigid Structure from Motion Probabilistic Point Set Registration with Prior Correspondences Point Set Registration Relying on Principles of Particle Dynamics Target Groups Scientists and students in the fields of computer vision and graphics, machine learning, applied mathematics as well asrelated fields Practitioners in industrial research and development in these fields About the Author Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras). . | ||
650 | 0 | _aComputer vision. | |
650 | 0 | _aVirtual reality. | |
650 | 0 | _aAugmented reality. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMachine learning. | |
650 | 1 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aVirtual and Augmented Reality. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMachine Learning. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783658305666 |
776 | 0 | 8 |
_iPrinted edition: _z9783658305680 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-658-30567-3 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c175505 _d175505 |