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DTSTAMP:20240214T070247Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T111500
DTEND;TZID=Australia/Melbourne:20231214T112500
UID:siggraphasia_SIGGRAPH Asia 2023_sess149_papers_816@linklings.com
SUMMARY:Lock-free Vertex Clustering for Multicore Mesh Reduction
DESCRIPTION:Technical Papers\n\nNima Fathollahi and Sean Chester (Universi
 ty of Victoria)\n\nModern data collection methods can capture representati
 ons of 3D objects at resolutions much greater than they can be discretely 
 rendered as an image. To improve the efficiency of storage, transmission, 
 rendering, and editing of 3D models constructed from such data, it is bene
 ficial to first employ a mesh reduction technique to reduce the size of a 
 mesh. Vertex clustering, a technique that merges close vertices together, 
 has particularly wide applicability, because it operates only on vertices 
 and their spatial proximity. However, it is also very difficult to acceler
 ate with parallelisation in a deterministic manner because it contains ext
 ensive algorithmic dependencies.\n\nPrior work treats the non-trivial clus
 tering step of this process serially to preserve vertex priorities, which 
 fundamentally limits to mid-single digits the acceleration rates that are 
 possible for the process overall. This paper introduces a novel lock-free 
 parallel algorithm, P-Weld, that exposes parallelism with a graph-theoreti
 c lens that iteratively peels away layers of a mesh that have no remaining
  dependencies. Concurrent updates to shared data are managed with a linear
 isable sequence of atomic instructions that exactly reproduces the serial 
 clustering. The resulting parallelism and improved spatial locality yield 
 a 3.86× speed-up on a standard 14-million vertex mesh and a 2.93× speed-up
  on a 400-million vertex LiDaR point cloud covering the city of Vancouver,
  Canada, relative to a popular open source library.\n\nRegistration Catego
 ry: Full Access\n\nSession Chair: Marco ATTENE (Institute for Applied Math
 ematics and Information Technologies (IMATI), CNR)
URL:https://asia.siggraph.org/2023/full-program?id=papers_816&sess=sess149
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