000 03607nam a22005415i 4500
001 978-3-658-30567-3
003 DE-He213
005 20240423125220.0
007 cr nn 008mamaa
008 200604s2020 gw | s |||| 0|eng d
020 _a9783658305673
_9978-3-658-30567-3
024 7 _a10.1007/978-3-658-30567-3
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
100 1 _aGolyanik, Vladislav.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXXIV, 352 p. 119 illus., 13 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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