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Path Guiding


Practical Product Path Guiding Using Linearly Transformed Cosines

Stavros Diolatzis, Adrien Gruson, Wenzel Jakob, Derek Nowrouzezahrai, George Drettakis

Path tracing is now the standard method used to generate realistic imagery in many domains, e.g., film, special effects, architecture etc. Path guiding has recently emerged as a powerful strategy to counter the notoriously long computation times required to render such images. In this paper we present a practical path guiding algorithm that performs product sampling, i.e., samples based on the product of the bidirectional scattering distribution function (BSDF) and incoming radiance. We use a spatial-directional subdivision to represent incoming radiance as in previous work, and introduce the use of Linear Transformed Cosines (LTCs) to represent the BSDF during path guiding, thus enabling efficient product sampling. Despite the computational efficiency of LTCs, several optimizations are needed to make our method worthwhile. In particular, we show how we can use vectorization, precomputation, as well as strategies to optimize multiple importance sampling and russian roulette to improve performance. We evaluate our method on several different scenes; the results show overall gains in efficiency, especially in scenes with significant inter-reflection between glossy objects.

Temporal Sample Reuse for Next Event Estimation and Path Guiding for Real-Time Path Tracing

Addis Dittebrandt, Johannes Hanika, Carsten Dachsbacher

Good importance sampling is crucial for real-time path tracing where only low sample budgets are possible. We present two efficient sampling techniques tailored for massively-parallel GPU path tracing which improve next event estimation (NEE) for rendering with many light sources and sampling of indirect illumination. As sampling density needs to vary spatially, we use an octree structure in world space and introduce algorithms to continuously adapt the partitioning and distribution of the sampling budget. Both sampling techniques exploit temporal coherence by reusing samples from the previous frame: For NEE we collect unoccluded samples on light sources and show how to deduplicate, but also diffuse this information to efficiently sample light sources in the subsequent frame. For sampling indirect illumination, we present a compressed directional quadtree structure which is iteratively adapted towards high-energy directions using samples from the previous frame. The updates and rebuilding of all data structures takes about 1 ms in our test scenes, and adds about 6 ms at 1080p to the path tracing time compared to using state-of-the-art light hierarchies and BRDF sampling. We show that this additional effort reduces noise in terms of mean squared error by at least one order of magnitude in many situations.

Adaptive Caustics Rendering in Production with Photon Guiding

Alejandro Conty, Christopher Kulla

We present a user controlled technique for achieving fast and stable caustics in a production renderer for both surface and participating media. We combine a progressive photon mapping approach with emission guiding in an on-demand framework hat avoids the raytracing overhead of robust bidirectional systems. We also contribute modifications to turn bias into noise while speeding up the render, making the result usable for both adaptive picture refinement and denoisers.