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DTSTAMP:20260114T163632Z
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_591@linklings.com
SUMMARY:Discontinuity-Aware 2D Neural Fields
DESCRIPTION:Yash Belhe (University of California San Diego); Michael Gharb
 i, Matt Fisher, and Iliyan Georgiev (Adobe Inc.); and Ravi Ramamoorthi and
  Tzu-Mao Li (University of California San Diego)\n\nNeural image represent
 ations offer the possibility of high-fidelity, compact storage, and resolu
 tion-independent accuracy, providing an attractive alternative to traditio
 nal pixel and grid-based representations.  \nHowever, coordinate neural ne
 tworks fail to capture discontinuities present in the image and tend to bl
 ur across them; we aim to address this challenge.\nFor many applications, 
 such as representing a resolution-independent rendered image, vector graph
 ics, diffusion curves, or solutions to partial differential equations, we 
 already know the locations of the discontinuities.\nWe take the discontinu
 ity locations as input, represented as linear, quadratic, or cubic Bezier 
 curves,  and construct a feature field that is only discontinuous across t
 hese locations, and smooth everywhere else.\nFinally, we use a shallow mul
 ti-layer perceptron to decode the features into the signal value.\nFor the
  feature field construction, we develop a new data structure based on a cu
 rved triangular mesh with features stored on the vertices and a subset of 
 the edges of the mesh being marked discontinuous.\nWe show that our method
  can be used to compress a 100k^2 rendered image into a 25MB file; \ncan b
 e used as a new diffusion curve solver by combining with Monte-Carlo-based
  methods or directly supervised by the diffusion curve energy;\nor can be 
 used for compressing 2D physics simulation data.\n\nRegistration Category:
  Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhibitor\
 n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_591&sess=sess209
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