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X-WR-CALNAME:Learning Generative Models of 3D Shapes: From Implicit Functio
 ns to Structured Representations - Richard (Hao) Zhang\, Professor\, SFU
BEGIN:VTIMEZONE
TZID:America/Vancouver
TZUNTIL:20221106T090000Z
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TZNAME:PST
DTSTART:20201101T020000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RDATE:20211107T020000
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DTSTART:20200308T020000
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RDATE:20220313T020000
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DTSTAMP:20251220T054738Z
CLASS:PUBLIC
CREATED:20210127T231719Z
DESCRIPTION:Please register for this event here. Abstract: Unlike images an
 d video\, 3D shapes are not confined to one standard representation. This 
 is one of the challenges we face when developing deep neural networks (DNN
 s) to learn generative models of 3D shapes or virtual scenes. So far\, vox
 el grids\, multi-view images\, point clouds\, and integrated surface patch
 es have all been considered. In this talk\, I show that traditional convol
 utional neural networks operating on pixels/voxels may not be best suited 
 for the task. I first present IM-Net\, our recent work on learning implici
 t functions\, and show the…
DTSTART;TZID=America/Vancouver:20210222T130000
DTEND;TZID=America/Vancouver:20210222T140000
LAST-MODIFIED:20210324T232202Z
LOCATION:Please register to receive the Zoom link
SUMMARY:Learning Generative Models of 3D Shapes: From Implicit Functions to
  Structured Representations - Richard (Hao) Zhang\, Professor\, SFU
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/learning-generative-models-3d-shapes-implici
 t-functions-structured-representations-richard
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