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DTSTART:19911015T033000
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BEGIN:VEVENT
DTSTAMP:20251218T030653Z
LOCATION:Meeting Room S426+S427\, Level 4
DTSTART;TZID=Asia/Hong_Kong:20251216T111200
DTEND;TZID=Asia/Hong_Kong:20251216T112300
UID:siggraphasia_SIGGRAPH Asia 2025_sess118_papers_1053@linklings.com
SUMMARY:Jackknife Transmittance and MIS Weight Estimation
DESCRIPTION:Christoph Peters (Delft University of Technology)\n\nA core op
 eration in Monte Carlo volume rendering is transmittance estimation: Given
  a segment along a ray, the goal is to estimate the fraction of light that
  will pass through this segment without encountering absorption or out-sca
 ttering. A naive approach is to estimate optical depth τ using unbiased ra
 y marching and to then use exp(-τ) as transmittance estimate. However, thi
 s strategy systematically overestimates transmittance due to Jensen's ineq
 uality. On the other hand, existing unbiased transmittance estimators eith
 er suffer from high variance or have a cost governed by random decisions, 
 which makes them less suitable for SIMD architectures. We propose a biased
  transmittance estimator with significantly reduced bias compared to the n
 aive approach and a deterministic and low cost. We observe that ray marchi
 ng with stratified jittered sampling results in estimates of optical depth
  that are nearly normal-distributed. We then apply the unique minimum vari
 ance unbiased (UMVU) estimator of exp(-τ) based on two such estimates (usi
 ng two different sets of random numbers). Bias only arises from violations
  of the assumption of normal-distributed inputs. We further reduce bias an
 d variance using a variance-aware importance sampling scheme. The underlyi
 ng theory can be used to estimate any analytic function of optical depth. 
 We use this generalization to estimate multiple importance sampling (MIS) 
 weights and introduce two integrators: Unbiased MIS with biased MIS weight
 s and a more efficient but biased combination of MIS and transmittance est
 imation.\n\nRegistration Category: Full Access, Full Access Supporter\n\nS
 ession Chair: Oliver Deussen (University of Konstanz)\n\n
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