BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Australia/Melbourne
X-LIC-LOCATION:Australia/Melbourne
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:19721003T020000
RRULE:FREQ=YEARLY;BYMONTH=4;BYDAY=1SU
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:19721003T020000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20240214T070250Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T131500
DTEND;TZID=Australia/Melbourne:20231215T133000
UID:siggraphasia_SIGGRAPH Asia 2023_sess157_papers_378@linklings.com
SUMMARY:Learning based 2D Irregular Shape Packing
DESCRIPTION:Technical Papers, TOG\n\nZeshi Yang and Zherong Pan (Tencent A
 merica), Manyi Li (Shandong University), and Kui Wu and Xifeng Gao (Tencen
 t America)\n\n2D irregular shape packing is a necessary step to arrange UV
  patches of a 3D model within a texture atlas for memory-efficient appeara
 nce rendering in computer graphics. Being a joint, combinatorial decision-
 making problem involving all patch positions and orientations, this proble
 m has well-known NP-hard complexity. Prior solutions either assume a heuri
 stic packing order or modify the upstream mesh cut and UV mapping to simpl
 ify the problem, which either limits the packing ratio or incurs robustnes
 s or generality issues. Instead, we introduce a learning-assisted 2D irreg
 ular shape packing method that achieves a high packing quality with minima
 l requirements from the input. Our method iteratively selects and groups s
 ubsets of UV patches into near-rectangular super patches, essentially redu
 cing the problem to bin-packing, based on which a joint optimization is em
 ployed to further improve the packing ratio. In order to efficiently deal 
 with large problem instances with hundreds of patches, we train deep neura
 l policies to predict nearly rectangular patch subsets and determine their
  relative poses, leading to linear time scaling with the number of patches
 . We demonstrate the effectiveness of our method on 3 datasets for UV pack
 ing, where our method achieves higher packing ratio over several widely us
 ed baselines with competitive computational speed.\n\nRegistration Categor
 y: Full Access\n\nSession Chair: Chi Wing Fu (The Chinese University of Ho
 ng Kong)
URL:https://asia.siggraph.org/2023/full-program?id=papers_378&sess=sess157
END:VEVENT
END:VCALENDAR