Presentation
Many-Worlds Inverse Rendering
DescriptionDiscontinuous visibility changes remain a major bottleneck when optimizing surfaces within a physically based inverse renderer. Many previous works have proposed sophisticated algorithms and data structures to sample visibility silhouettes more efficiently.
Our work presents another solution: instead of differentiating a tentative surface locally, we differentiate a non-local perturbation of a surface. We refer to this as a “many-worlds” representation because it models a non-interacting superposition of conflicting explanations (“worlds”) of the input dataset. Each world is optically isolated from others, leading to a new transport law that distinguishes our method from prior work based on exponential random media.
The resulting Monte Carlo algorithm is simpler and more efficient than prior methods. We demonstrate that our method promotes rapid convergence, both in terms of the total iteration count and the cost per iteration.
Our work presents another solution: instead of differentiating a tentative surface locally, we differentiate a non-local perturbation of a surface. We refer to this as a “many-worlds” representation because it models a non-interacting superposition of conflicting explanations (“worlds”) of the input dataset. Each world is optically isolated from others, leading to a new transport law that distinguishes our method from prior work based on exponential random media.
The resulting Monte Carlo algorithm is simpler and more efficient than prior methods. We demonstrate that our method promotes rapid convergence, both in terms of the total iteration count and the cost per iteration.
Event Type
Technical Papers
TimeTuesday, 16 December 20251:20pm - 1:31pm HKT
LocationMeeting Room S426+S427, Level 4
