BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
X-LIC-LOCATION:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260805T205603Z
LOCATION:Room 408 B
DTSTART;TZID=America/Los_Angeles:20260720T154500
DTEND;TZID=America/Los_Angeles:20260720T155500
UID:siggraph_SIGGRAPH 2026_sess146_papers_1432@linklings.com
SUMMARY:Points as Tori: Fast Pointwise Signed Distance for Point Clouds
DESCRIPTION:Nicole Feng and Ioannis Gkioulekas (Carnegie Mellon University
 ) and Keenan Crane (Carnegie Mellon University, Roblox Research)\n\nSigned
  distance computation has been either fast or robust, but not both. We exp
 lain why, using a theory that unifies signed distance with reconstruction.
  Our method directly estimates signed distance from point clouds, providin
 g fast pointwise evaluation at arbitrary spatial resolution, without requi
 ring discretization, global optimization, or explicit reconstruction.\n\nI
 nterest Area: Research & Education\n\nKeyword: Artificial Intelligence/Mac
 hine Learning, Geometry, Modeling\n\nRegistration Category: Full Conferenc
 e Supporter, Full Conference\n\nSession Chair: Jernej Barbic (University o
 f Southern California)\n\n
END:VEVENT
END:VCALENDAR