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DD18 - SEED - Raytracing in Hybrid Real-Time Rendering

The document discusses the integration of ray tracing in hybrid real-time rendering, focusing on its application in a mini-game designed for AI agents. It covers the motivation for using ray tracing, highlights the goals of achieving clean visuals and procedural world generation, and outlines the hybrid rendering pipeline and material sampling techniques. The presentation emphasizes advancements in rendering technologies, global illumination, and challenges in achieving efficient and high-quality outputs in real-time applications.

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Stochastic all the things: Raytracing in hybrid real-time rendering Tomasz Stachowiak SEED – Electronic Arts
Watch the trailer here: https://www.youtube.com/watch?v=LXo0WdlELJk
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 “PICA PICA” ▪ Exploratory mini-game & world ▪ For our self-learning AI agents to play, not for humans ☺ ▪ “Imitation Learning with Concurrent Actions in 3D Games” [Harmer 2018] ▪ Uses SEED’s Halcyon R&D engine ▪ Goals ▪ Explore hybrid rendering with DXR ▪ Clean and consistent visuals ▪ Procedural worlds [Opara 2018] ▪ No precomputation
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Agenda ▪ Motivation ▪ Reflections ▪ Translucency and Transparency ▪ Global Illumination ▪ Shadows
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Why use raytracing?
Path tracing ~15 seconds accumulation
Path tracing (1 sample per pixel)
Real-time hybrid with raytracing
No raytracing
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Is this a fair comparison? ▪ “Classic” game solutions exist ▪ Local reflection volumes ▪ Planar reflections ▪ Lightmaps ▪ “Sky visibility” maps ▪ …
Zero artist overhead 1. Enable raytracing 2. ??? 3. PROFIT!
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Hybrid Rendering Pipeline Direct shadows (raytrace or raster) Direct lighting (compute) Reflections (raytrace or compute) Deferred shading (raster) Global Illumination (raytrace) Post processing (compute)Transparency & Translucency (raytrace) Ambient occlusion (raytrace or compute)
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Materials ▪ Multiple microfacet layers ▪ Rapidly experiment with different looks ▪ Bake down a number of layers for production ▪ Energy conserving ▪ Automatic Fresnel between layers ▪ Inspired by Arbitrarily Layered Micro- Facet Surfaces [Weidlich 2007] ▪ Unified for all lighting & rendering modes ▪ Raster, hybrid, path-traced reference Objects with Multi-Layered Materials
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Raytraced Reflections ▪ Launch rays from G-Buffer ▪ Raytrace at half resolution ▪ ¼ ray/pixel for reflection ▪ ¼ ray/pixel for reflected shadow ▪ Reconstruct at full resolution ▪ Arbitrary normals ▪ Spatially-varying roughness Raytraced Reflections
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Pipeline Importance sampling Screen-space reflection Raytracing Spatial reconstruction Envmap gap fill Bilateral cleanup Temporal accumulation
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT] ▪ Re-roll if ray below horizon
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT] ▪ Re-roll if ray below horizon
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Layered Material Sampling ▪ One ray for the whole stack ▪ Stochastically select a layer ▪ Based on layer visibility estimate ▪ Bottom layers occluded by top layers ▪ View-dependent due to Fresnel ▪ Approximate with Schlick ▪ Sample BRDF of selected layer ▪ Multiple layers can generate same direction ▪ Query each layer about generated direction ▪ Add up probabilities Multi-Layered Materials [Weidlich 2007]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Raygen ▪ Read generated ray ▪ TraceRay() ▪ Same hit shaders as path-tracing reference ▪ Output packed in 2x RGBA 16F Color R Color G Color B G-buffer Depth Dir X * Hit T Dir Y * Hit T Dir Z * Hit T Inverse PDF
Raw raytrace output
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Why Stochastic? ▪ Gives right answer, but ▪ Produces noise ▪ Thrashes caches ▪ Alternative: post-filter ▪ Needs arbitrarily large kernel ▪ Introduces bleeding ▪ Sensitive to aliasing ▪ Meet in the middle? ▪ Bias samplers ▪ Combine with spatial filtering ▪ High variance -> increase bias ▪ More research needed! Image Space Gathering [Robison 2009]
Raw raytrace output
+Spatial reconstruction
+Spatial reconstruction ▪ Based on Stochastic Screen-Space Reflections [Stachowiak 2015] ▪ For every full-res pixel, use 16 half-res ray hits ▪ Poisson-disk distribution around pixel ▪ Scale by local BRDF ▪ Divide by ray PDF ▪ Ratio estimator [Heitz 2018] result = 0.0 weightSum = 0.0 for pixel in neighborhood: weight = localBrdf(pixel.hit) / pixel.hitPdf result += color(pixel.hit) * weight weightSum += weight result /= weightSum
Same ray hits used in 2x2 quads Spatial upsampling
Disjoint sample sets in 2x2 quads Spatial upsampling
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes ▪ Class determines sample set
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes ▪ Class determines sample set ▪ Take 16 samples from each set in a disk ▪ Disjoint high-quality samples
Spatial reconstruction
+Temporal accumulation
+Bilateral cleanup
+Bilateral cleanup ▪ Secondary bilateral filter ▪ Much dumber than reconstruction ▪ Introduces blur ▪ Only run where variance high ▪ Variance from spatial reconstruction ▪ Temporally smoothed with hysteresis = 0.5 ▪ Variable kernel width, sample count
+Bilateral cleanup ▪ Secondary bilateral filter ▪ Much dumber than reconstruction ▪ Introduces blur ▪ Only run where variance high ▪ Variance from spatial reconstruction ▪ Temporally smoothed with hysteresis = 0.5 ▪ Variable kernel width, sample count Variance estimate
+Bilateral cleanup
+Temporal anti-aliasing
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Temporal Reprojection ▪ Two sources of velocity ▪ Per-pixel motion vector ▪ Reprojection of hit point
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Temporal Reprojection ▪ Neither reprojection method robust Per-pixel motion vector Reprojection of hit point
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Dual-Source Reprojection ▪ Sample history using both reprojection strategies ▪ Estimate local statistics during spatial reconstruction ▪ RGB mean ▪ RGB standard deviation ▪ Of used hit samples ▪ Weigh contributions ▪ rgb_dist = (rgb – rgb_mean) / rgb_dev ▪ dist = (rgb – rgb_mean) / rgb_dev ▪ w = exp2(-10 * luma(dist))
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reprojection Clamp ▪ Build color box from local statistics ▪ rgb_min = rgb_mean – rgb_dev ▪ rgb_max = rgb_mean + rgb_dev ▪ Clamp reprojected values to box ▪ rgb = clamp(rgb, rgb_min, rgb_max) ▪ Same idea as in temporal anti-aliasing [Karis 2014] ▪ Weigh clamped contributions ▪ rgb_sum += val * w ▪ Introduces bias ▪ Fixes most ghosting
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency & Translucency ▪ Raytracing enables accurate light scattering ▪ Refractions ▪ Order-independent (OIT) ▪ Variable roughness ▪ Handles multiple IOR transitions ▪ Beer-Lambert absorption ▪ Translucency ▪ Light scattering inside a medium ▪ Inspired by Translucency in Frostbite [Barré-Brisebois 2011] Glass and Translucency
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Integration ▪ Multiple samples required for rough refractions and translucency ▪ Multi-layer screen-filtering complicated ▪ Layered framebuffers? ▪ Per-pixel linked lists? ▪ Filter in 2D or between layers? ▪ More research needed! ▪ Accumulate in texture-space instead ▪ Stable integration domain ▪ Static texture allocation ▪ 512*512 per object ▪ Time-sliced, ~1M rays/frame
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function)
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection ▪ Gather all samples
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection ▪ Gather all samples ▪ Update value in texture
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Filtering ▪ Can denoise spatially and/or temporally ▪ Temporal: build an update heuristic ▪ Reactive enough for moving lights & objects ▪ Exponential moving average can be OK ▪ Variance-adaptive mean estimation ▪ Based on exponential moving average ▪ Adaptive hysteresis
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency ▪ Similar approach to translucency ▪ Launch ray using view’s origin and direction ▪ Refract based on medium's index of refraction ▪ Sample a BSDF for rough refraction ▪ Trace a ray in the scene & sample lighting ▪ Tint the result ▪ Chromatic aberration from interface ▪ Beer-Lambert absorption in medium
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency ▪ Similar approach to translucency ▪ Launch ray using view’s origin and direction ▪ Refract based on medium's index of refraction ▪ Sample a BSDF for rough refraction ▪ Trace a ray in the scene & sample lighting ▪ Tint the result ▪ Chromatic aberration from interface ▪ Beer-Lambert absorption in medium ▪ Pen: we don’t handle transparent shadows yet
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Global Illumination ▪ Important for consistency ▪ Minimize artist overhead ▪ We want a technique with: ▪ No precomputation ▪ No parametrization (UVs, proxies) ▪ Support for static and dynamic scenes ▪ Adaptive refinement
Indirect diffuse off
Indirect diffuse on
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Storage ▪ Can’t afford to solve every frame ▪ Our GI budget: 250k rays / frame ▪ World space vs screen space ▪ World space is stable ▪ Screen-space fully dynamic ▪ World space surfels ▪ Easy to accumulate ▪ Distribute on the fly ▪ No parameterization ▪ Smooth result by construction ▪ Position, normal, radius, animation info
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Skinning
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Skinning
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Screen Application ▪ Render like deferred light sources ▪ Diffuse only ▪ Smoothstep distance attenuation ▪ Mahalanobis metric ▪ Squared dot of normals angular weight ▪ Inspired by [Lehtinen 2008] ▪ World-space 3D culling ▪ Uniform grid ▪ Each cell holds list of surfels ▪ Find one cell per pixel, use all from list
Combine with Screen Space AO [Jimenez 2016]
Combine with Screen Space AO [Jimenez 2016]
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel ▪ Find lowest coverage in tile (16x16 pixels)
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel ▪ Find lowest coverage in tile (16x16 pixels) ▪ Probabilistically spawn surfel on pixel ▪ Chance proportional to pixel’s projected area ▪ G-Buffer depth and normal ▪ Continue where coverage below threshold
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Irradiance Calculation ▪ Path trace from surfels ▪ Unidirectional, explicit light connections ▪ More samples for new surfels Scene geometry Surfel Sky Light source
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Irradiance Calculation ▪ Path trace from surfels ▪ Unidirectional, explicit light connections ▪ More samples for new surfels ▪ Limit light path length ▪ Sample previous GI at last bounce ▪ "Infinite" bounces amortized over frames Query surfels at hit point Surfel Light source
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Monte Carlo Integration ▪ Monte Carlo assumes immutable integrand ▪ Plain old mean estimation ▪ Can’t use in dynamic GI ▪ Hard reset undesirable Interactive GI in Frostbite [Hillaire 2018] ҧ𝑥0 = 0 ҧ𝑥 𝑛+1 = 𝑙𝑒𝑟𝑝 ҧ𝑥 𝑛, 𝑥 𝑛+1, 1 𝑛 + 1
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Adaptive Integration ▪ Give up on fully converging ▪ Exponential moving average ▪ Estimate short-term statistics ▪ Mean, variance ▪ Adapt blend factor 𝑘 ▪ Reduce if variance high ▪ Increase if mean far from short-term statistics ҧ𝑥0 = 0 ҧ𝑥 𝑛+1 = 𝑙𝑒𝑟𝑝 ҧ𝑥 𝑛, 𝑥 𝑛+1, 𝑘
Adaptive integration in 1D Exponential moving averageAdaptive mean Short-term distribution bounds
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadows ▪ Hard shadows trivial ▪ Launch a ray towards light position ▪ Check for any hit ▪ Soft shadows easy ▪ Sample direction from uniform cone [PBRT] ▪ Cone width determines penumbra
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadows ▪ Hard shadows trivial ▪ Launch a ray towards light position ▪ Check for any hit ▪ Soft shadows easy ▪ Sample direction from uniform cone [PBRT] ▪ Cone width determines penumbra ▪ … Easy but noisy ▪ 1 visibility sample per pixel not enough ▪ Filter instead of shooting more
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadow Filtering ▪ Based on Nvidia’s SVGF [Schied 2017] ▪ Temporal accumulation ▪ Multi-pass weighted blur ▪ Kernel size driven by variance estimate ▪ No shadow-specific heuristics Accumulate temporally Blur Estimate variance Blur some more (Over)simplified diagram of SVGF Input Output
Hard Raytraced Shadows Soft Raytraced Shadows (Unfiltered) Soft Raytraced Shadows (Filtered)
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadow Filtering ▪ Modified to reduce ghosting ▪ TAA-like color clip ▪ Variance-based bounds (5x5 kernel) [Salvi 2016] Baseline SVGF +TAA-like clip
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Summary ▪ Replace fine-tuned hacks with unified approaches ▪ Reconstruction and filtering instead ▪ New tool in the box ▪ Solve sparse and incoherent problems ▪ Not a silver bullet ▪ Use rays wisely ▪ Pica Pica shoots ~2.25 rays per pixel ▪ Real-time visuals with (almost) path traced quality achievable today
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Thanks ▪ SEED ▪ Johan Andersson ▪ Colin Barré-Brisebois ▪ Jasper Bekkers ▪ Joakim Bergdahl ▪ Ken Brown ▪ Dean Calver ▪ Dirk de la Hunt ▪ Jenna Frisk ▪ Paul Greveson ▪ Henrik Halen ▪ Effeli Holst ▪ Andrew Lauritzen ▪ Magnus Nordin ▪ Niklas Nummelin ▪ Anastasia Opara ▪ Kristoffer Sjöö ▪ Ida Winterhaven ▪ Graham Wihlidal ▪ Microsoft ▪ Chas Boyd ▪ Ivan Nevraev ▪ Amar Patel ▪ Matt Sandy ▪ NVIDIA ▪ Tomas Akenine-Möller ▪ Nir Benty ▪ Jiho Choi ▪ Peter Harrison ▪ Alex Hyder ▪ Jon Jansen ▪ Aaron Lefohn ▪ Ignacio Llamas ▪ Henry Moreton ▪ Martin Stich
S E E D / / S E A R C H F O R E X T R A O R D I N A R Y E X P E R I E N C E S D I V I S I O N S T O C K H O L M – L O S A N G E L E S – M O N T R É A L – R E M O T E W W W . E A . C O M / S E E D W E ‘ R E H I R I N G !
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Questions?
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 References▪ [Harmer 2018] Jack Harmer, Linus Gisslén, Henrik Holst, Joakim Bergdahl, Tom Olsson, Kristoffer Sjöö and Magnus Nordin“ Imitation Learning with Concurrent Actions in 3D Games”. available online ▪ [Barré-Brisebois 2011] Barré-Brisebois, Colin and Bouchard, Marc. “Approximating Translucency for a Fast, Cheap and Convincing Subsurface Scattering Look”, available online. ▪ [Barré-Brisebois 2017] Barré-Brisebois, Colin. “A Certain Slant of Light: Past, Present and Future Challenges of Global Illumination in Games”, available online. ▪ [Igehy 1999] Igehy, Homan. “Tracing Ray Differentials”, available online. ▪ [PBRT] Pharr, Matt. Jakob, Wenzel and Humphreys, Greg. “Physically Based Rendering”, Book, http://www.pbrt.org/. ▪ [Schied 2017] Schied, Christoph et. Al. “Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination”, NVIDIA Research, available online. ▪ [Stachowiak 2015] Stachowiak, Tomasz. “Stochastic Screen-Space Reflections”, available online. ▪ [Weidlich 2007] Weidlich, Andrea and Wilkie, Alexander. “Arbitrarily Layered Micro-Facet Surfaces”, available online. ▪ [Williams 1983] Williams, Lance. “Pyramidal Parametrics”, available online. ▪ [Robison 2009] Austin Robison, Peter Shirley. "Image Space Gathering", available online. ▪ [Heitz 2018] Eric Heitz, Stephen Hill, Morgan McGuire. "Combining Analytic Direct Illumination and Stochastic Shadows", available online. ▪ [Karis 2014] Brian Karis. "High-Quality Temporal Supersampling", available online. ▪ [Lehtinen 2008] Jaakko Lehtinen, Matthias Zwicker, Emmanuel Turquin, Janne Kontkanen, Frédo Durand, François Sillion, Timo Aila. "A Meshless Hierarchical ▪ Representation for Light Transport“, available online. ▪ [Jimenez 2016] Jorge Jimenez, Xian-Chun Wu, Angelo Pesce, Adrian Jarabo. "Practical Realtime Strategies for Accurate Indirect Occlusion", available online. ▪ [Opara 2018] Anastasia Opara. “Creativity of Rules and Patterns”, available online. ▪ [Salvi 2016] Marco Salvi. "An excursion in temporal super sampling", available online. ▪ [Hillaire 2018] Sébastien Hillaire. "Real-time Raytracing for Interactive Global Illumination Workflows in Frostbite", available online.
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Bonus
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail What about texture level of detail? ▪ Mipmapping [Williams 1983] is the standard method to avoid texture aliasing: ▪ Screen-space pixel maps to approximately one texel in the mipmap hierarchy ▪ Supported by all GPUs for rasterization via shading quad and derivatives Left: level-of-detail (λ), partial derivatives and the parallelogram-approximated texture-space footprint of a pixel. Right: mipmap chain
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail No shading quads for ray tracing! ▪ Traditionaly: Ray Differentials ▪ Estimates the footprint of a pixel by computing world-space derivatives of the ray with respect to the image plane ▪ Have to differentiate (virtual offset) rays ▪ Heavier payload (12 floats) for subsequent rays (can) affect performance. Optimize! ▪ Alternative: always sample mip 0 with bilinear filtering (with extra samples) ▪ Leads to aliasing and additional performance cost Ray Differentials [Igehy99] Surface P(x + ∂x) D(x + ∂x)P(x) ∂D(x) ∂P(x) D(x) D(x) x y R(x) R(x + ∂x) ∂x
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail Together with Research, we developed a texture LOD technique for raytracing: ▪ Heuristic based on triangle properties, a curvature estimate, distance, and incident angle ▪ Similar quality to ray differentials with single trilinear lookup ▪ Single value stored in the payload for subsequent rays ▪ Upcoming publication by: ▪ Tomas Akenine-Möller (NV), Magnus Andersson (NV), Colin Barré-Brisebois (EA), Jim Nilsson (NV), Robert Toth (NV) Ground Truth, Ray Differentials, Ours, Mip0
S E E D // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 mGPU ▪ Explicit Heterogenous Multi-GPU ▪ Parallel Fork-Join Style ▪ Resources copied through system memory using copy queue ▪ Minimize PCI-E transfers ▪ Approach ▪ Run ray generation on primary GPU ▪ Copy results in sub-regions to other GPUs ▪ Run tracing phases on separate GPUs ▪ Copy tracing results back to primary GPU ▪ Run filtering on primary GPU Ray Generation Copy Sub Regions Copy Sub Regions GPU1 GPU2 GPU3 GPU4 Trace GPU 2 Trace GPU 1 Trace GPU 3 Trace GPU 4 Filter

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In this document
Powered by AI

Overview of raytracing in hybrid real-time rendering, focusing on AI agent interactions and procedural world goals.

Outlines of motivation, reflections, and main topics like translucency, transparency, global illumination, and shadows.

Discussion on why raytracing is essential in rendering techniques.

Path tracing methods including accumulation and real-time hybrid techniques compared with classic solutions.

Addresses zero artist overhead by enabling raytracing and the associated benefits.

Details of a hybrid rendering pipeline including shadow computations, reflections, deferred shading.

Highlights of multiple microfacet material layers, raytraced reflections, reconstruction at full resolution.

Explains microfacet importance sampling techniques, discussing ray tracing efficiency and sampling strategies.

Introduces stochastic methods for noise handling and spatial reconstruction in raytracing.

Details on sample patterns for spatial upsampling and noise reduction.

Integration of spatial and temporal cleanup methods to enhance rendering quality.

Raytracing methodology for transparency and translucency, discussing refractions, light scattering, and absorption.

Strategies for global illumination in rendering, focusing on ray budgeting and spatial distribution.

Discussion on surfel placement algorithms, irradiance calculation techniques, and path tracing.

Techniques for creating hard and soft shadows, shadow filtering processes to improve quality.

Summarizes the benefits of unified approaches in raytracing, highlighting real-time visuals.

Acknowledges contributions and references the works cited throughout the presentation.

Explains texture level-of-detail techniques and multi-GPU strategies for optimizing raytracing performance.

DD18 - SEED - Raytracing in Hybrid Real-Time Rendering

  • 1.
    Stochastic all thethings: Raytracing in hybrid real-time rendering Tomasz Stachowiak SEED – Electronic Arts
  • 3.
    Watch the trailerhere: https://www.youtube.com/watch?v=LXo0WdlELJk
  • 4.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 “PICA PICA” ▪ Exploratory mini-game & world ▪ For our self-learning AI agents to play, not for humans ☺ ▪ “Imitation Learning with Concurrent Actions in 3D Games” [Harmer 2018] ▪ Uses SEED’s Halcyon R&D engine ▪ Goals ▪ Explore hybrid rendering with DXR ▪ Clean and consistent visuals ▪ Procedural worlds [Opara 2018] ▪ No precomputation
  • 5.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Agenda ▪ Motivation ▪ Reflections ▪ Translucency and Transparency ▪ Global Illumination ▪ Shadows
  • 6.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Why use raytracing?
  • 7.
    Path tracing ~15seconds accumulation
  • 8.
    Path tracing (1sample per pixel)
  • 9.
  • 10.
  • 11.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Is this a fair comparison? ▪ “Classic” game solutions exist ▪ Local reflection volumes ▪ Planar reflections ▪ Lightmaps ▪ “Sky visibility” maps ▪ …
  • 12.
    Zero artist overhead 1.Enable raytracing 2. ??? 3. PROFIT!
  • 13.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Hybrid Rendering Pipeline Direct shadows (raytrace or raster) Direct lighting (compute) Reflections (raytrace or compute) Deferred shading (raster) Global Illumination (raytrace) Post processing (compute)Transparency & Translucency (raytrace) Ambient occlusion (raytrace or compute)
  • 14.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Materials ▪ Multiple microfacet layers ▪ Rapidly experiment with different looks ▪ Bake down a number of layers for production ▪ Energy conserving ▪ Automatic Fresnel between layers ▪ Inspired by Arbitrarily Layered Micro- Facet Surfaces [Weidlich 2007] ▪ Unified for all lighting & rendering modes ▪ Raster, hybrid, path-traced reference Objects with Multi-Layered Materials
  • 15.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Raytraced Reflections ▪ Launch rays from G-Buffer ▪ Raytrace at half resolution ▪ ¼ ray/pixel for reflection ▪ ¼ ray/pixel for reflected shadow ▪ Reconstruct at full resolution ▪ Arbitrary normals ▪ Spatially-varying roughness Raytraced Reflections
  • 16.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Pipeline Importance sampling Screen-space reflection Raytracing Spatial reconstruction Envmap gap fill Bilateral cleanup Temporal accumulation
  • 17.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
  • 18.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
  • 19.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
  • 20.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
  • 21.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT]
  • 22.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT] ▪ Re-roll if ray below horizon
  • 23.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Sampling ▪ Microfacet importance sampling ▪ Normal distribution function (NDF) part of BRDF ▪ Very few rays, so quality matters ▪ Low-discrepancy Halton sequence ▪ Cranley-Patterson rotation per pixel [PBRT] ▪ Re-roll if ray below horizon
  • 24.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Layered Material Sampling ▪ One ray for the whole stack ▪ Stochastically select a layer ▪ Based on layer visibility estimate ▪ Bottom layers occluded by top layers ▪ View-dependent due to Fresnel ▪ Approximate with Schlick ▪ Sample BRDF of selected layer ▪ Multiple layers can generate same direction ▪ Query each layer about generated direction ▪ Add up probabilities Multi-Layered Materials [Weidlich 2007]
  • 25.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reflection Raygen ▪ Read generated ray ▪ TraceRay() ▪ Same hit shaders as path-tracing reference ▪ Output packed in 2x RGBA 16F Color R Color G Color B G-buffer Depth Dir X * Hit T Dir Y * Hit T Dir Z * Hit T Inverse PDF
  • 26.
  • 27.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Why Stochastic? ▪ Gives right answer, but ▪ Produces noise ▪ Thrashes caches ▪ Alternative: post-filter ▪ Needs arbitrarily large kernel ▪ Introduces bleeding ▪ Sensitive to aliasing ▪ Meet in the middle? ▪ Bias samplers ▪ Combine with spatial filtering ▪ High variance -> increase bias ▪ More research needed! Image Space Gathering [Robison 2009]
  • 28.
  • 29.
  • 30.
    +Spatial reconstruction ▪ Basedon Stochastic Screen-Space Reflections [Stachowiak 2015] ▪ For every full-res pixel, use 16 half-res ray hits ▪ Poisson-disk distribution around pixel ▪ Scale by local BRDF ▪ Divide by ray PDF ▪ Ratio estimator [Heitz 2018] result = 0.0 weightSum = 0.0 for pixel in neighborhood: weight = localBrdf(pixel.hit) / pixel.hitPdf result += color(pixel.hit) * weight weightSum += weight result /= weightSum
  • 31.
    Same ray hitsused in 2x2 quads Spatial upsampling
  • 32.
    Disjoint sample setsin 2x2 quads Spatial upsampling
  • 33.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes
  • 34.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes
  • 35.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes ▪ Class determines sample set
  • 36.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Sample Pattern ▪ Four sample sets for pixels in 2x2 quad ▪ Threshold blue noise into four classes ▪ Class determines sample set ▪ Take 16 samples from each set in a disk ▪ Disjoint high-quality samples
  • 37.
  • 38.
  • 39.
  • 40.
    +Bilateral cleanup ▪ Secondarybilateral filter ▪ Much dumber than reconstruction ▪ Introduces blur ▪ Only run where variance high ▪ Variance from spatial reconstruction ▪ Temporally smoothed with hysteresis = 0.5 ▪ Variable kernel width, sample count
  • 41.
    +Bilateral cleanup ▪ Secondarybilateral filter ▪ Much dumber than reconstruction ▪ Introduces blur ▪ Only run where variance high ▪ Variance from spatial reconstruction ▪ Temporally smoothed with hysteresis = 0.5 ▪ Variable kernel width, sample count Variance estimate
  • 42.
  • 43.
  • 44.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Temporal Reprojection ▪ Two sources of velocity ▪ Per-pixel motion vector ▪ Reprojection of hit point
  • 45.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Temporal Reprojection ▪ Neither reprojection method robust Per-pixel motion vector Reprojection of hit point
  • 46.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Dual-Source Reprojection ▪ Sample history using both reprojection strategies ▪ Estimate local statistics during spatial reconstruction ▪ RGB mean ▪ RGB standard deviation ▪ Of used hit samples ▪ Weigh contributions ▪ rgb_dist = (rgb – rgb_mean) / rgb_dev ▪ dist = (rgb – rgb_mean) / rgb_dev ▪ w = exp2(-10 * luma(dist))
  • 47.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Reprojection Clamp ▪ Build color box from local statistics ▪ rgb_min = rgb_mean – rgb_dev ▪ rgb_max = rgb_mean + rgb_dev ▪ Clamp reprojected values to box ▪ rgb = clamp(rgb, rgb_min, rgb_max) ▪ Same idea as in temporal anti-aliasing [Karis 2014] ▪ Weigh clamped contributions ▪ rgb_sum += val * w ▪ Introduces bias ▪ Fixes most ghosting
  • 48.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency & Translucency ▪ Raytracing enables accurate light scattering ▪ Refractions ▪ Order-independent (OIT) ▪ Variable roughness ▪ Handles multiple IOR transitions ▪ Beer-Lambert absorption ▪ Translucency ▪ Light scattering inside a medium ▪ Inspired by Translucency in Frostbite [Barré-Brisebois 2011] Glass and Translucency
  • 49.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Integration ▪ Multiple samples required for rough refractions and translucency ▪ Multi-layer screen-filtering complicated ▪ Layered framebuffers? ▪ Per-pixel linked lists? ▪ Filter in 2D or between layers? ▪ More research needed! ▪ Accumulate in texture-space instead ▪ Stable integration domain ▪ Static texture allocation ▪ 512*512 per object ▪ Time-sliced, ~1M rays/frame
  • 50.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal
  • 51.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside
  • 52.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function)
  • 53.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection
  • 54.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection ▪ Gather all samples
  • 55.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Breakdown ▪ For every valid position & normal ▪ Flip normal and push (ray) inside ▪ Launch rays in uniform sphere dist. ▪ (Importance-sample phase function) ▪ Compute lighting at intersection ▪ Gather all samples ▪ Update value in texture
  • 56.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Translucency Filtering ▪ Can denoise spatially and/or temporally ▪ Temporal: build an update heuristic ▪ Reactive enough for moving lights & objects ▪ Exponential moving average can be OK ▪ Variance-adaptive mean estimation ▪ Based on exponential moving average ▪ Adaptive hysteresis
  • 57.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency ▪ Similar approach to translucency ▪ Launch ray using view’s origin and direction ▪ Refract based on medium's index of refraction ▪ Sample a BSDF for rough refraction ▪ Trace a ray in the scene & sample lighting ▪ Tint the result ▪ Chromatic aberration from interface ▪ Beer-Lambert absorption in medium
  • 58.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Transparency ▪ Similar approach to translucency ▪ Launch ray using view’s origin and direction ▪ Refract based on medium's index of refraction ▪ Sample a BSDF for rough refraction ▪ Trace a ray in the scene & sample lighting ▪ Tint the result ▪ Chromatic aberration from interface ▪ Beer-Lambert absorption in medium ▪ Pen: we don’t handle transparent shadows yet
  • 59.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Global Illumination ▪ Important for consistency ▪ Minimize artist overhead ▪ We want a technique with: ▪ No precomputation ▪ No parametrization (UVs, proxies) ▪ Support for static and dynamic scenes ▪ Adaptive refinement
  • 60.
  • 61.
  • 62.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Spatial Storage ▪ Can’t afford to solve every frame ▪ Our GI budget: 250k rays / frame ▪ World space vs screen space ▪ World space is stable ▪ Screen-space fully dynamic ▪ World space surfels ▪ Easy to accumulate ▪ Distribute on the fly ▪ No parameterization ▪ Smooth result by construction ▪ Position, normal, radius, animation info
  • 63.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Skinning
  • 64.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Skinning
  • 65.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Screen Application ▪ Render like deferred light sources ▪ Diffuse only ▪ Smoothstep distance attenuation ▪ Mahalanobis metric ▪ Squared dot of normals angular weight ▪ Inspired by [Lehtinen 2008] ▪ World-space 3D culling ▪ Uniform grid ▪ Each cell holds list of surfels ▪ Find one cell per pixel, use all from list
  • 66.
    Combine with ScreenSpace AO [Jimenez 2016]
  • 67.
    Combine with ScreenSpace AO [Jimenez 2016]
  • 68.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
  • 69.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
  • 70.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Surfel Spawning From Camera @ 1% speed
  • 71.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling
  • 72.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
  • 73.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
  • 74.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel
  • 75.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel ▪ Find lowest coverage in tile (16x16 pixels)
  • 76.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Surfel Placement Algorithm ▪ Iterative screen-space hole filling ▪ Calculate surfel coverage for each pixel ▪ Find lowest coverage in tile (16x16 pixels) ▪ Probabilistically spawn surfel on pixel ▪ Chance proportional to pixel’s projected area ▪ G-Buffer depth and normal ▪ Continue where coverage below threshold
  • 77.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Irradiance Calculation ▪ Path trace from surfels ▪ Unidirectional, explicit light connections ▪ More samples for new surfels Scene geometry Surfel Sky Light source
  • 78.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Irradiance Calculation ▪ Path trace from surfels ▪ Unidirectional, explicit light connections ▪ More samples for new surfels ▪ Limit light path length ▪ Sample previous GI at last bounce ▪ "Infinite" bounces amortized over frames Query surfels at hit point Surfel Light source
  • 79.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Monte Carlo Integration ▪ Monte Carlo assumes immutable integrand ▪ Plain old mean estimation ▪ Can’t use in dynamic GI ▪ Hard reset undesirable Interactive GI in Frostbite [Hillaire 2018] ҧ𝑥0 = 0 ҧ𝑥 𝑛+1 = 𝑙𝑒𝑟𝑝 ҧ𝑥 𝑛, 𝑥 𝑛+1, 1 𝑛 + 1
  • 80.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Adaptive Integration ▪ Give up on fully converging ▪ Exponential moving average ▪ Estimate short-term statistics ▪ Mean, variance ▪ Adapt blend factor 𝑘 ▪ Reduce if variance high ▪ Increase if mean far from short-term statistics ҧ𝑥0 = 0 ҧ𝑥 𝑛+1 = 𝑙𝑒𝑟𝑝 ҧ𝑥 𝑛, 𝑥 𝑛+1, 𝑘
  • 81.
    Adaptive integration in1D Exponential moving averageAdaptive mean Short-term distribution bounds
  • 82.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadows ▪ Hard shadows trivial ▪ Launch a ray towards light position ▪ Check for any hit ▪ Soft shadows easy ▪ Sample direction from uniform cone [PBRT] ▪ Cone width determines penumbra
  • 83.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadows ▪ Hard shadows trivial ▪ Launch a ray towards light position ▪ Check for any hit ▪ Soft shadows easy ▪ Sample direction from uniform cone [PBRT] ▪ Cone width determines penumbra ▪ … Easy but noisy ▪ 1 visibility sample per pixel not enough ▪ Filter instead of shooting more
  • 84.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadow Filtering ▪ Based on Nvidia’s SVGF [Schied 2017] ▪ Temporal accumulation ▪ Multi-pass weighted blur ▪ Kernel size driven by variance estimate ▪ No shadow-specific heuristics Accumulate temporally Blur Estimate variance Blur some more (Over)simplified diagram of SVGF Input Output
  • 85.
    Hard Raytraced ShadowsSoft Raytraced Shadows (Unfiltered) Soft Raytraced Shadows (Filtered)
  • 86.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Shadow Filtering ▪ Modified to reduce ghosting ▪ TAA-like color clip ▪ Variance-based bounds (5x5 kernel) [Salvi 2016] Baseline SVGF +TAA-like clip
  • 87.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Summary ▪ Replace fine-tuned hacks with unified approaches ▪ Reconstruction and filtering instead ▪ New tool in the box ▪ Solve sparse and incoherent problems ▪ Not a silver bullet ▪ Use rays wisely ▪ Pica Pica shoots ~2.25 rays per pixel ▪ Real-time visuals with (almost) path traced quality achievable today
  • 88.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Thanks ▪ SEED ▪ Johan Andersson ▪ Colin Barré-Brisebois ▪ Jasper Bekkers ▪ Joakim Bergdahl ▪ Ken Brown ▪ Dean Calver ▪ Dirk de la Hunt ▪ Jenna Frisk ▪ Paul Greveson ▪ Henrik Halen ▪ Effeli Holst ▪ Andrew Lauritzen ▪ Magnus Nordin ▪ Niklas Nummelin ▪ Anastasia Opara ▪ Kristoffer Sjöö ▪ Ida Winterhaven ▪ Graham Wihlidal ▪ Microsoft ▪ Chas Boyd ▪ Ivan Nevraev ▪ Amar Patel ▪ Matt Sandy ▪ NVIDIA ▪ Tomas Akenine-Möller ▪ Nir Benty ▪ Jiho Choi ▪ Peter Harrison ▪ Alex Hyder ▪ Jon Jansen ▪ Aaron Lefohn ▪ Ignacio Llamas ▪ Henry Moreton ▪ Martin Stich
  • 89.
    S E ED / / S E A R C H F O R E X T R A O R D I N A R Y E X P E R I E N C E S D I V I S I O N S T O C K H O L M – L O S A N G E L E S – M O N T R É A L – R E M O T E W W W . E A . C O M / S E E D W E ‘ R E H I R I N G !
  • 90.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Questions?
  • 91.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 References▪ [Harmer 2018] Jack Harmer, Linus Gisslén, Henrik Holst, Joakim Bergdahl, Tom Olsson, Kristoffer Sjöö and Magnus Nordin“ Imitation Learning with Concurrent Actions in 3D Games”. available online ▪ [Barré-Brisebois 2011] Barré-Brisebois, Colin and Bouchard, Marc. “Approximating Translucency for a Fast, Cheap and Convincing Subsurface Scattering Look”, available online. ▪ [Barré-Brisebois 2017] Barré-Brisebois, Colin. “A Certain Slant of Light: Past, Present and Future Challenges of Global Illumination in Games”, available online. ▪ [Igehy 1999] Igehy, Homan. “Tracing Ray Differentials”, available online. ▪ [PBRT] Pharr, Matt. Jakob, Wenzel and Humphreys, Greg. “Physically Based Rendering”, Book, http://www.pbrt.org/. ▪ [Schied 2017] Schied, Christoph et. Al. “Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination”, NVIDIA Research, available online. ▪ [Stachowiak 2015] Stachowiak, Tomasz. “Stochastic Screen-Space Reflections”, available online. ▪ [Weidlich 2007] Weidlich, Andrea and Wilkie, Alexander. “Arbitrarily Layered Micro-Facet Surfaces”, available online. ▪ [Williams 1983] Williams, Lance. “Pyramidal Parametrics”, available online. ▪ [Robison 2009] Austin Robison, Peter Shirley. "Image Space Gathering", available online. ▪ [Heitz 2018] Eric Heitz, Stephen Hill, Morgan McGuire. "Combining Analytic Direct Illumination and Stochastic Shadows", available online. ▪ [Karis 2014] Brian Karis. "High-Quality Temporal Supersampling", available online. ▪ [Lehtinen 2008] Jaakko Lehtinen, Matthias Zwicker, Emmanuel Turquin, Janne Kontkanen, Frédo Durand, François Sillion, Timo Aila. "A Meshless Hierarchical ▪ Representation for Light Transport“, available online. ▪ [Jimenez 2016] Jorge Jimenez, Xian-Chun Wu, Angelo Pesce, Adrian Jarabo. "Practical Realtime Strategies for Accurate Indirect Occlusion", available online. ▪ [Opara 2018] Anastasia Opara. “Creativity of Rules and Patterns”, available online. ▪ [Salvi 2016] Marco Salvi. "An excursion in temporal super sampling", available online. ▪ [Hillaire 2018] Sébastien Hillaire. "Real-time Raytracing for Interactive Global Illumination Workflows in Frostbite", available online.
  • 92.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Bonus
  • 93.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail What about texture level of detail? ▪ Mipmapping [Williams 1983] is the standard method to avoid texture aliasing: ▪ Screen-space pixel maps to approximately one texel in the mipmap hierarchy ▪ Supported by all GPUs for rasterization via shading quad and derivatives Left: level-of-detail (λ), partial derivatives and the parallelogram-approximated texture-space footprint of a pixel. Right: mipmap chain
  • 94.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail No shading quads for ray tracing! ▪ Traditionaly: Ray Differentials ▪ Estimates the footprint of a pixel by computing world-space derivatives of the ray with respect to the image plane ▪ Have to differentiate (virtual offset) rays ▪ Heavier payload (12 floats) for subsequent rays (can) affect performance. Optimize! ▪ Alternative: always sample mip 0 with bilinear filtering (with extra samples) ▪ Leads to aliasing and additional performance cost Ray Differentials [Igehy99] Surface P(x + ∂x) D(x + ∂x)P(x) ∂D(x) ∂P(x) D(x) D(x) x y R(x) R(x + ∂x) ∂x
  • 95.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 Texture Level-of-Detail Together with Research, we developed a texture LOD technique for raytracing: ▪ Heuristic based on triangle properties, a curvature estimate, distance, and incident angle ▪ Similar quality to ray differentials with single trilinear lookup ▪ Single value stored in the payload for subsequent rays ▪ Upcoming publication by: ▪ Tomas Akenine-Möller (NV), Magnus Andersson (NV), Colin Barré-Brisebois (EA), Jim Nilsson (NV), Robert Toth (NV) Ground Truth, Ray Differentials, Ours, Mip0
  • 96.
    S E ED // Stochastic all the things: Raytracing in hybrid real-time rendering Programming and Technology Track, Digital Dragons 2018 mGPU ▪ Explicit Heterogenous Multi-GPU ▪ Parallel Fork-Join Style ▪ Resources copied through system memory using copy queue ▪ Minimize PCI-E transfers ▪ Approach ▪ Run ray generation on primary GPU ▪ Copy results in sub-regions to other GPUs ▪ Run tracing phases on separate GPUs ▪ Copy tracing results back to primary GPU ▪ Run filtering on primary GPU Ray Generation Copy Sub Regions Copy Sub Regions GPU1 GPU2 GPU3 GPU4 Trace GPU 2 Trace GPU 1 Trace GPU 3 Trace GPU 4 Filter