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PRODID:Linklings LLC
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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Room 324\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T153000
DTEND;TZID=Asia/Seoul:20221206T170000
UID:siggraphasia_SIGGRAPH Asia 2022_sess155_papers_322@linklings.com
SUMMARY:Differentiable Rendering of Neural SDFs through Reparameterization
DESCRIPTION:Technical Communications, Technical Papers\n\nDifferentiable R
 endering of Neural SDFs through Reparameterization\n\nBangaru, Gharbi, Lua
 n, Li, Sunkavalli...\n\nWe present a method to automatically compute corre
 ct gradients with respect to geometric scene parameters in neural SDF rend
 erers. Recent physically-based differentiable rendering techniques for mes
 hes have used edge-sampling to handle discontinuities, particularly at obj
 ect silhouettes, but SDFs do not have a simple parametric form amenable to
  sampling. Instead, our approach builds on area-sampling techniques and de
 velops a continuous warping function for SDFs to account for these discont
 inuities. Our method leverages the distance to surface encoded in an SDF a
 nd uses quadrature on sphere tracer points to compute this warping functio
 n. We further show that this can be done by subsampling the points to make
  the method tractable for neural SDFs. Our differentiable renderer can be 
 used to optimize neural shapes from multi-view images and produces compara
 ble 3D reconstructions to recent SDF-based inverse rendering methods, with
 out the need for 2D segmentation masks to guide the geometry optimization 
 and no volumetric approximations to the geometry.\n\nRegistration Category
 : FULL ACCESS, ON-DEMAND ACCESS\n\nLanguage: ENGLISH\n\nFormat: IN-PERSON,
  ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_322&sess=sess15
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