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DTSTAMP:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_378@linklings.com
SUMMARY:Learning based 2D Irregular Shape Packing
DESCRIPTION:Technical Papers\n\nZeshi Yang and Zherong Pan (Tencent Americ
 a), Manyi Li (Shandong University), and Kui Wu and Xifeng Gao (Tencent Ame
 rica)\n\n2D irregular shape packing is a necessary step to arrange UV patc
 hes of a 3D model within a texture atlas for memory-efficient appearance r
 endering in computer graphics. Being a joint, combinatorial decision-makin
 g problem involving all patch positions and orientations, this problem has
  well-known NP-hard complexity. Prior solutions either assume a heuristic 
 packing order or modify the upstream mesh cut and UV mapping to simplify t
 he problem, which either limits the packing ratio or incurs robustness or 
 generality issues. Instead, we introduce a learning-assisted 2D irregular 
 shape packing method that achieves a high packing quality with minimal req
 uirements from the input. Our method iteratively selects and groups subset
 s of UV patches into near-rectangular super patches, essentially reducing 
 the problem to bin-packing, based on which a joint optimization is employe
 d to further improve the packing ratio. In order to efficiently deal with 
 large problem instances with hundreds of patches, we train deep neural pol
 icies to predict nearly rectangular patch subsets and determine their rela
 tive poses, leading to linear time scaling with the number of patches. We 
 demonstrate the effectiveness of our method on 3 datasets for UV packing, 
 where our method achieves higher packing ratio over several widely used ba
 selines with competitive computational speed.\n\nRegistration Category: Fu
 ll Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_378&sess=sess209
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