Bruce Walter Pramook Khungurn Kavita Bala Cornell University

Bidirectional LightcutsBruce Walter Pramook Khungurn Kavita Bala

Cornell University

Friday, August 10, 2012

Realistic Rendering Challenge

• Complex light sources• Global illumination• Wide range of materials

2Images: Autodesk® 360 Rendering & Autodesk® Homestyler®Friday, August 10, 2012

Realistic Rendering Challenge

• Complex light sources• Global illumination• Wide range of materials– Glossy, subsurface, volumetric ...

3Friday, August 10, 2012

Equal Time Comparison

Probabilistic Photon Map

Bidirectional Path Trace

MultidimensionalLightcuts (VPL)

[Knaus & Zwicker 11] [Walter et al. 06] [Veach & Guibas 94,95]

New MethodBidirectional Path Trace

Goal: Combine Strengths

• Multidimensional Lightcuts– Biased virtual point light (VPL) method✓Low noise and scalable performance

• Bidirectional Path Tracing– Noisy and slow to converge✓Supports wide range of materials

Bidirectional Lightcuts

• Bidirectional VPL algorithm– Bidirectional estimators– Novel weighting scheme‣Control bias vs. noise tradeoff

– Scalable and low noise‣ Integrated with Lightcuts

Outline

• Prior work: VPL and Bidirectional review• New weighting strategy• Integration with Multidimensional Lightcuts• Results

Virtual Point Light Methods

• Approximate global illumination using point lights

Camera

Light Source

• Approximate global illumination using point lights• Generate point lights (VPL)

Light Source

• Approximate global illumination using point lights• Generate point lights (VPL)• Generate point sensors (VPS)

Camera VPS

• Approximate global illumination using point lights• Generate point lights (VPL)• Generate point sensors (VPS)• Gather illumination

• More VPLs = more accurate

Camera

Material Appearance Problem

Naive VPL(no clamping)

Standard VPL(with clamping)

- Clamping distorts material appearance [Krivanek et al. 10]Friday, August 10, 2012

Standard VPL(with clamping)

Our Result

- Clamping distorts material appearance [Krivanek et al. 10]Friday, August 10, 2012

Prior VPL Work– Adaptive VPL generation‣ [Segovia et al. 06, Davidovic et al. 10]

– Modified VPLs‣ [Hasan et al. 09]

– VPL bias compensation‣ [Kollig & Keller 04, Engelhardt et al. 10, Novak et al. 11]

– Specialized BSSRDF extension‣ [Arbree et al. 08]

– Hybrid algorithms‣ [Dammertz et al. 10]

Bidirectional Path Tracing Review

• Bidirectional Path Tracing

Camera

Light Source

• Bidirectional Path Tracing– Trace path from light

• Bidirectional Path Tracing– Trace path from light– Trace path from camera

• Bidirectional Path Tracing– Trace path from light– Trace path from camera– Sum over all connections

• Multiple ways to generate each path

• Multiple ways to generate each path– Combine using weights: wi

‣ e.g., Balance heuristic– Best connection depends on path– Unbiased if: ∑wi = 1

• Advantages– Wider range of paths handled efficiently

• Disadvantages– Increased cost per sample– Some paths are still hard to find‣May be noisy or slow to converge

Bidirectional VPL Rendering

• Standard VPL rendering– Limited subset of bidirectional methods– Clamping causes weight sum < 1

• Bidirectional extension

• Bidirectional extension– Unbiased weighting [Kollig & Keller 04]‣ Low noise properties of VPL rendering is lost

Outline

New Weighting Strategy

• Designed for low noise while minimizing bias

• Four weight constraints– 1) Energy conservation– 2) Clamping– 3) Diffuse VPLs– 4) Exclude high variance eye paths

• Actual weight is the minimum of constraints33

First Weight Constraint

• 1) Energy conservation– Weight sum ≤ 1– Reduce amortized cost ‣Prefer shorter eye paths

Lower cost

Higher cost

Second Weight Constraint

• 2) Clamping– Control VPL noise‣Matches previous clamping [Walter et al. 06]

35No clamping With clampingFriday, August 10, 2012

Third Weight Constraint

• 3) Diffuse VPLs:– Primarily a cost optimization‣Narrowly directional VPLs are inefficient‣Typically expensive with little effect

Glossy Surface

• 3) Diffuse VPLs:– Primarily a cost optimization‣Narrowly directional VPLs are inefficient‣Typically expensive with little effect

– New diffuse vs. glossy classifier‣ Independent of material model‣Preserves Lambertian, isotropic, etc.‣Applicable to other rendering algorithms

Third Weight Constraint

• 4) Exclude high variance eye paths:– Control VPS noise– Based on directional spread heuristic

‣ 4a) Narrow eye ray spread– E.g., sharp glossy reflections– Unrestricted recursion

‣ 4b) Wide eye ray spread– E.g., Subsurface, diffuse indirect– Restrict to short range effects only

Fourth Weight Constraint

39Narrow gloss Broad gloss

Diffuse Back Wall

Plate 1 Plate 2 Plate 3

Standard VPL(not bidirectional)

Narrow gloss Broad gloss

Diffuse Back Wall

Plate 1 Plate 2 Plate 3

Without constraint 4(unbiased)

Narrow gloss Broad glossFriday, August 10, 2012

With Constraint 4

Without constraint 4(unbiased)

Narrow gloss Broad glossFriday, August 10, 2012

Subsurface Extensions

• Path-based representation– 2 vertices plus special segment

• New BSSRDF sampling method– Ray tracing random chords on spheres

• Applicable to other path-based algorithms

Subsurface

Outline

Multidimensional Lightcuts

• Millions of sensor/light pairs per pixel– Brute force → expensive

• Multidimensional Lightcuts– Evaluate small subset of pairs

Grand Central [Walter et al. 05]0.03% of sensor/light pairs evaluated

Multidimensional Lightcuts

• Cluster points into hierarchies

• Adaptive select cut (partition)– Bound max error per cluster– Refine until below perceptual threshold

• Sublinear cost per point– Used in Autodesk® 360 Rendering‣A million images this year 46

Cluster Tree

Number of Points

NaiveLightcut

Lightcuts Integration

• Lightcut extensions– New material types – Added weighting terms to bounding and evaluation– Quad-tree cubemaps representation for bounds

Cubemap Unfolded quad-tree cubemap

Outline

Results: Accurate Materials

Standard VPL Our Method

Glossy

Subsurface

Glossy

Subsurface

Volumetric

Anisotropic volumetric fiber model (giga-voxel resolution) Friday, August 10, 2012

Standard VPL

Bidirectional Estimators

Standard VPL

• Accurate rendering of complex materials- e.g., glossy counter, subsurface milk, volumetric cloth

Our Method

Bidirectional Estimators

Timing Results

55Images 512x512, 1 million VPLs, 64 eye rays per pixel, 4 cores at 3.4GHz

Standard VPL

Timing Results

Standard VPL

Ball Dragon

Timing Results

Standard VPL

Ball Dragon SanMiguel

Timing Results

Standard VPL

Ball Dragon SanMiguel Kitchen

Timing Results

Lightcuts (standard) 346s 359s 434s 1076sBidirectional 479s (+38%) 745s (+107%) 618s (+42%) 1803s (+67%)

Images 512x512, 1 million VPLs, 64 eye rays per pixel, 4 cores at 3.4GHzFriday, August 10, 2012

Limitations and Future Work

• Noise control relies on heuristics– Effective in our tests but without guarantees

• Cannot handle some phenomena (e.g., caustics)– VPL methods are ill-suited to such paths– Combine with specialized algorithms

Conclusion

• VPL-based rendering has many advantages– Low noise– Scalable performance– Limited in materials and effects

• Bidirectional extension– Wider range of materials– Enable new applications

The End

• Acknowledgements– Models and assistance:

‣ Edgar Velazquez-Armendariz, Shuang Zhao, Adam Arbree, John Hutchinson, Jaroslav Krivanek, Guillermo M. Leal Llaguno, Stanford Graphics Lab

– Funded by:‣ National Science Foundation‣ Autodesk‣ Intel Science and Technology Center -

Visual Computing62

Summary

• Bidirectional VPL algorithm– Recursive sensor generation– Novel weighting scheme‣Control bias vs. noise tradeoff

– Scalable and low noise‣ Integrated with Lightcuts

• General techniques– New BSSRDF sampling– Model independent diffuse classifier

Progressive Photon Map Our Method Bidirectional Path Trace

Enlarged

Bruce Walter Pramook Khungurn Kavita Bala Cornell University

Documents

Lecture 11: Irradiance Caching - Cornell University · 1 Lecture 11: Irradiance Caching CS 6620, Spring 2009 Kavita Bala Computer Science Cornell University © Kavita Bala, Computer

Lec 9: Pipelining Kavita Bala CS 3410, Fall 2008 Computer Science Cornell University

CS4620/5620: Lecture 5 3D Transforms (Rotations) · © 2012 Kavita Bala • (with previous instructors James/Marschner) Cornell CS4620/5620 Fall 2012 • Lecture 5 1 CS4620/5620:

Shadow Volumes - Cornell University · slide courtesy of Kavita Bala, Cornell University Shadow Volumes • Accurate shadows • Clever counting method using stencil buffer • Can

© Kavita Bala, Computer Science, Cornell University Kevin Walsh CS 3410, Spring 2010 Computer Science Cornell University Pipelining See: P&H Chapter 4.5

Shading Annotations in the Wild - Amazon Web …labelmaterial.s3.amazonaws.com/.../saw_paper.pdfShading Annotations in the Wild Balazs Kovacs Sean Bell Noah Snavely Kavita Bala Cornell

Fast Agglomerative Clustering for Rendering Bruce Walter, Kavita Bala, Cornell University Milind Kulkarni, Keshav Pingali University of Texas, Austin

Prof. Kavita Bala and Prof. Hakim Weatherspoon CS 3410, Spring 2014 Computer Science Cornell University See PH Appendix B.8 (register files) and B.9

arXiv:1703.07511v3 [cs.CV] 11 Apr 2017elishe@adobe.com Kavita Bala Cornell University kb@cs.cornell.edu Figure 1: Given a reference style image (a) and an input image (b), we seek

Lec16 Hardware Projects - cs. · PDF fileKavita Bala Computer Science Cornell University © Kavita Bala, Computer Science, Cornell University Announcements • Project proposal due

Monte Carlo Techniques for Renderingmathinfo.univ-reims.fr/image/siIllumination/Complements/Cours Cornell...3 © Kavita Bala, Computer Science, Cornell University Intersection Acceleration

1 Single Scattering in Refractive Media with Triangle Mesh Boundaries Bruce Walter Shuang Zhao Nicolas Holzschuch Kavita Bala Cornell Univ. Grenoble Univ

CS4670: Computer Vision Kavita Bala Lecture 7: Harris Corner Detection

CS4621/5621 Fall 2015 - Cornell University€¦ · CS4621/5621 Fall 2015 Basics of OpenGL/GLSL Shading and Lighting Professor: Kavita Bala Instructor: Nicolas Savva with slides from

Lecture 4: Reflection Models - Cornell University1 Lecture 4: Reflection Models CS 6620, Spring 2009 Kavita Bala Computer Science Cornell University © Kavita Bala, Computer Science,

3D Transforms - Cornell University Transforms © 2011 Kavita Bala • (with previous instructors James/Marschner) Cornell CS4620/5620 Fall 2011 •!Lecture 10 Announcements

Lecture 3: Radiometry – Wed: 2-3 Upson 5142€¦ · 1 Lecture 3: Radiometry Fall 2004 Kavita Bala Computer Science Cornell University © Kavita Bala, Computer Science, Cornell University

Balazs Kovacs Sean Bell Noah Snavely Kavita Bala Cornell ... · Balazs Kovacs Sean Bell Noah Snavely Kavita Bala Cornell University Abstract Understanding shading effects in images

Lecture 11: Transformations CS4670/5760: Computer Vision Kavita Bala

CS4670: Computer Vision Kavita Bala Lecture 8: Scale invariance