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SUMMARY:Daniel Cremers (TU Munich)
DTSTART:20210615T101500Z
DTEND:20210615T114500Z
DTSTAMP:20260423T022603Z
UID:MathDeep/9
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MathDeep/9/"
 >Self-supervised Learning for 3D Shape Analysis</a>\nby Daniel Cremers (TU
  Munich) as part of Mathematics of Deep Learning\n\n\nAbstract\nWhile neur
 al networks have swept the field of computer vision and replaced classical
  methods in most areas of image analysis and beyond\, extending their powe
 r to the domain of 3D shape analysis remains an important open challenge. 
 In my presentation\, I will focus on the problems of shape matching\, corr
 espondence estimation and shape interpolation and develop suitable deep le
 arning approaches to tackle these challenges. In particular\, I will focus
  on the difficult problem of computing correspondence and interpolation fo
 r pairs of shapes from different classes — say a human and a horse — w
 here traditional isometry assumptions no longer hold.\n
LOCATION:https://researchseminars.org/talk/MathDeep/9/
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