Next Event Estimation++

Next Event Estimation++: Visibility Mapping for Efficient Light Transport Simulation

Guo, Jerry Jinfeng (TU Delft Computer Graphics and Visualisation)
Eisemann, M. (TU Braunschweig)
Eisemann, E. (TU Delft Computer Graphics and Visualisation)

2020

Monte-Carlo rendering requires determining the visibility between scene points as the most common and compute intense operation to establish paths between camera and light source. Unfortunately, many tests reveal occlusions and the corresponding paths do not contribute to the final image. In this work, we present next event estimation++ (NEE++): a visibility mapping technique to perform visibility tests in a more informed way by caching voxel to voxel visibility probabilities. We show two scenarios: Russian roulette style rejection of visibility tests and direct importance sampling of the visibility. We show applications to next event estimation and light sampling in a uni-directional path tracer, and light-subpath sampling in Bi-Directional Path Tracing. The technique is simple to implement, easy to add to existing rendering systems, and comes at almost no cost, as the required information can be directly extracted from the rendering process itself. It discards up to 80% of visibility tests on average, while reducing variance by 20% compared to other state-of-the-art light sampling techniques with the same number of samples. It gracefully handles complex scenes with efficiency similar to Metropolis light transport techniques but with a more uniform convergence.

CCS Concepts
Visibility
bi-directional path tracing
path tracing
rendering
shadowray
• Computing methodologies → Ray tracing

http://resolver.tudelft.nl/uuid:34910768-a6fb-4d8a-b7ec-ba4408476833

https://doi.org/10.1111/cgf.14138

2021-12-13

1467-8659

Computer Graphics Forum (online), 39 (7), 205-217

Institutional Repository

journal article

© 2020 Jerry Jinfeng Guo, M. Eisemann, E. Eisemann