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VERSION:2.0
PRODID:Linklings LLC
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TZID:Asia/Hong_Kong
X-LIC-LOCATION:Asia/Hong_Kong
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:19911015T033000
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BEGIN:VEVENT
DTSTAMP:20251218T030656Z
LOCATION:Meeting Room S421\, Level 4
DTSTART;TZID=Asia/Hong_Kong:20251217T132000
DTEND;TZID=Asia/Hong_Kong:20251217T133100
UID:siggraphasia_SIGGRAPH Asia 2025_sess137_papers_2099@linklings.com
SUMMARY:Efficient and Scalable Spatial Regularization of Optimal Transport
DESCRIPTION:Lucas Brifault (Inria, Dassault Systèmes); David Cohen-Steiner
  (Inria); and Mathieu Desbrun (Inria, Ecole Polytechnique)\n\nIn this pape
 r, we introduce a novel approach to spatial regularization of \noptimal tr
 ansport problems. Based on the notion of forward and backward "mean maps" 
 of a transport plan, we introduce a convex formulation of optimal transpor
 t problems that incorporates regularization of these mean maps to promote 
 spatial continuity of the resulting optimal plan. Unlike previous regulari
 zation approaches that required the optimization of all the transport plan
  coefficients, our formulation translates into an ADMM-based solver combin
 ed with Sinkhorn type algorithms, which drastically reduces the number of 
 variables and scales up to large problems. We demonstrate the usefulness a
 nd efficiency of this new computational tool for various applications and 
 for different regularizations.\n\nRegistration Category: Full Access, Full
  Access Supporter\n\nSession Chair: Oded Stein (University of Southern Cal
 ifornia)\n\n
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