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DTSTAMP:20260114T163701Z
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:Nima Fathollahi and Sean Chester (University of Victoria)\n\nM
 odern data collection methods can capture representations 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 editi
 ng of 3D models constructed from such data, it is beneficial to first empl
 oy a mesh reduction technique to reduce the size of a mesh. Vertex cluster
 ing, a technique that merges close vertices together, has particularly wid
 e applicability, because it operates only on vertices and their spatial pr
 oximity. However, it is also very difficult to accelerate with parallelisa
 tion in a deterministic manner because it contains extensive algorithmic d
 ependencies.\n\nPrior work treats the non-trivial clustering step of this 
 process serially to preserve vertex priorities, which fundamentally limits
  to mid-single digits the acceleration rates that are possible for the pro
 cess overall. This paper introduces a novel lock-free parallel algorithm, 
 P-Weld, that exposes parallelism with a graph-theoretic lens that iterativ
 ely peels away layers of a mesh that have no remaining dependencies. Concu
 rrent updates to shared data are managed with a linearisable sequence of a
 tomic instructions that exactly reproduces the serial clustering. The resu
 lting 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 ve
 rtex LiDaR point cloud covering the city of Vancouver, Canada, relative to
  a popular open source library.\n\nRegistration Category: Full Access\n\nS
 ession Chair: Marco ATTENE (Institute for Applied Mathematics and Informat
 ion Technologies (IMATI), CNR)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_816&sess=sess149
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