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CS4340 Digital Special Effects

Semester 2, 2011/2012

School of Computing National University of Singapore

Realistic Rendering of Synthetic Objects into

Real Scenes

Guest Lecture by Low Kok Lim

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Goal

To put synthetic objects (computer rendered objects) into

pictures or video of real scenes such that results "look

right"

Need to match

Scale

Camera motion

intrinsic & extrinsic

parameters

Illumination

[Frank Vitz, 2003]

Mystique in X-Men 2

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real computer

generated real computer

generated

Taken from http://www.virtualcinematography.org/publications/acrobat/BRDF-s2003.pdf

Mr. Smith

from

Matrix

Reloaded

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Match Illumination

Old (labor-intensive) methods

Manually survey positions of light sources, and instantiate

similar virtual lights to light virtual objects

Photograph a neutral reference object in the scene, and

use it as a guide to manually configure a lighting

environment

Reflection mapping

Cannot easily simulate indirect illumination effects

between real and virtual objects

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Image-Based Lighting (IBL)

Solves the problem by

"faithfully" recording the

scene radiance

In a High-Dynamic Range

Light Probe Image

Use the recorded scene

radiance to light the

synthetic objects

[Paul Debevec, 2002]

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Light probe image

A frame of the short film "Rendering with Natural Light"

http://www.debevec.org/RNL/

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Light probe image

[Debevec1998]

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Overview of IBL Steps

1. Acquire background photographs or video

2. Acquire and assemble the light probe image

3. Construct light-based model

Map the light probe to an emissive surface surrounding

the scene

4. Identify local scene and model its geometry and

reflectance

5. Render the scene as illuminated by the IBL

environment

6. Postprocess, tone map and composite the renderings

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Detour

We will come back to the details of the IBL steps later

Need to first understand

High-dynamic range imaging

For faithful recording of scene radiance

Global illumination

For realistic rendering of synthetic objects and part of real

scene

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High Dynamic Range Imaging (HDRI)

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Motivation

Ordinary cameras cannot record wide

range of scene radiance in one image

Typically only 8-11 stops (EV)

Solution: Take multiple images of different exposures

(different exposure times) and "combine" them

Multiple exposures HDR image

Tone-mapped image Images from http://www.cambridgeincolour.com/tutorials/high-dynamic-range.htm

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Results

Combining the multiple exposures, we get

Irradiance at each pixel (unknown scale)

The HDR image

Camera response function

R, G, B channels are generally different

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Example

Exposures from 30 sec to 1/1000 sec, at 1-stop

increment

[Devebec1997]

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Example

Response functions of a Fuji 100 ASA negative film

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Example

The HDR image (the

false colors show

relative radiance

values)

Dynamic range about

25,000:1 (>14 stops)

[Devebec1997]

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Example

Tone-mapped

image

Input images

Images from http://en.wikipedia.org/wiki/Tone_mapping

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Application of HDRI

Recovery of surface BRDF

Image processing and photography

Exposures after image acquisition

Images from http://en.wikipedia.org/wiki/High_dynamic_range_image

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Application of HDRI

Blurring (e.g. simulating out-of-focus)

Motion Blur

Images from http://en.wikipedia.org/wiki/High_dynamic_range_image

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Application of HDR Images

More realistic

rendering

HDR rendering

supported in

hardware

Images from http://en.wikipedia.org/wiki/High_dynamic_range_rendering

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HDRI References

Books

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting by Erik Reinhard, Greg Ward, Sumanta Pattanaik, and Paul Debevec, 2005

Software Tools

HDR Shop: http://gl.ict.usc.edu/HDRShop/

Photoshop CS2: http://www.adobe.com/products/photoshop/

Photomatix: http://www.hdrsoft.com/

HDR Image Formats

ILM OpenEXR (.exr): http://www.openexr.com/

RADIANCE RGBE (.hdr or .rgbe): http://radsite.lbl.gov/radiance/

Papers

[Devebec1997]

Paul Devebec et al., "Recovering High Dynamic Range Radiance Maps from Photographs," SIGGRAPH '97

[Mitsunaga1999]

Tomoo Mitsunaga et al., "Radiometric Self Calibration," CVPR '99

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Global Illumination

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Global Illumination

Evaluating light reflected from a point x by taking into

consideration all illumination that arrives at the point

Figure by Frédo Durand, MIT

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The Rendering Equation

Mathematical formulation of global illumination

Integrate over the

hemisphere around x

x

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The Rendering Equation

Cannot be evaluated analytically

In practice, send tons of random rays (Monte Carlo

methods)

It is recursive

To evaluate Lref (x, ref), we need to evaluate Lin (x', in),

and so on

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Some Lighting Effects

Color bleeding caused by

diffuse-to-diffuse interactions

Caustics caused by

focusing of light

[Henrik Jensen]

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Global Illumination Algorithms

Ray-tracing approach

Whitted ray tracing [Whitted1980]

Distributed ray tracing [Cook1984]

Path tracing [Kajiya1986]

Two-pass ray tracing [Arvo1986]

Photon mapping [Jensen1995]*

not a complete GI algorithm

Finite-element approach

Radiosity [Goral1984]

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Path Tracing

For each pixel, shoot multiple random primary rays

At each intersection, only a secondary ray is shot

The secondary ray can be in any direction, not just sampled from

the specular lobe

Each primary ray from the eye and its subsequent secondary

rays form a light path

The ray tree has

branching factor of one

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Path Tracing

Simulates complete global illumination

But at very high computational cost

Indirect illumination, such as caustics, exhibits high variance

10 paths / pixel

[Henrik Jensen]

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Radiosity

Implements only diffuse-diffuse interactions

Scene is discretized into patches, and interaction

between patches are considered

Global illumination solution is computed by solving a set

of linear equations

Solution is view independent and consists of a constant

radiosity (W/m2) for every patch in the scene

Once solution is computed, it can be viewed from any view

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Radiosity Images

[Cornell University Program of Computer Graphics]

The Cornell Box

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Global Illumination References

Books

Advanced Global Illumination, Second Edition by Philip Dutré, Kavita Bala, Philippe Bekaert, 2006

Physically Based Rendering: From Theory to Implementation by Matt Pharr & Greg Humphreys, 2004

Realistic Ray Tracing, 2nd Edition by Peter Shirley & R. Keith Morley, 2003

Realistic Image Synthesis Using Photon Mapping by Henrik Wann Jensen, 2001

Principles of Digital Image Synthesis by Andrew S. Glassner, 1995

Radiosity and Realistic Image Synthesis by Michael F. Cohen & John R. Wallace, 1993

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Global Illumination References

Non-Commercial Renderers

YafRay: http://www.yafray.org/

RADIANCE: http://radsite.lbl.gov/radiance/

PBRT (Physically-Based Raytracer): http://www.pbrt.org/

POV-Ray: http://www.povray.org/ (v3.6 does not support HDR IBL)

MegaPOV: http://megapov.inetart.net/

Indigo Renderer: http://www.indigorenderer.com/

Commercial Renderers

Mental Ray: http://www.mentalimages.com/

Pixar's RenderMan: https://renderman.pixar.com/

Maxwell Renderer: http://www.maxwellrender.com/

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Global Illumination References

Papers

Rendering equation [Kajiya1986]

J. T. Kajiya, "The Rendering Equation," SIGGRAPH '86

Whitted ray tracing [Whitted1980]

T. Whitted, "An Improved Illumination Model for Shaded Display," Comm. ACM, 23(6):343-349, 1980

Distributed ray tracing [Cook1984]

R. Cook et al., "Distributed Ray Tracing," SIGGRAPH '84

Radiosity [Goral1984]

C. Goral et al., "Modeling the Interaction of Light Between Diffuse Surfaces," SIGGRAPH '84

Path tracing [Kajiya1986]

Two-pass ray tracing [Arvo1986]

J. Arvo, "Backwards Ray Tracing," Developments in Ray Tracing, SIGGRAPH '86 Course Notes #12

Photon mapping [Jensen1995]

H. W. Jensen et al., "Photn Maps in Bidirectional Monte Carlo Ray Tracing of Complex Objects," Computer & Graphics 19(2):215-224, 1995

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Image-Based Lighting (IBL)

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Overview of IBL Steps

1. Acquire background photographs or video

2. Acquire and assemble the light probe image

3. Construct light-based model

Map the light probe to an emissive surface surrounding

the scene

4. Identify local scene and model its geometry and

reflectance

5. Render the scene as illuminated by the IBL

environment

6. Postprocess, tone map and composite the renderings

36

Use this example

to demonstrate

the IBL steps

[Debevec1998]

Example

Synthetic objects

Background photo

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1. Acquire Background Photograph

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2. Acquire Light Probe Image (HDR)

Light probe image The pattern is for camera calibration.

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3. Construct Light-Based Model

Need to have an

approximate 3D

model of the

environment

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Separation of Scene

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4. Identify Local Scene

Model its geometry and estimate its reflectance

References

Yizhou Yi et al., "Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs," SIGGRAPH '99

Paul Devebec et al., "Estimating Surface Reflectance Properties of s Complex Scene Under Natural Illumination," ACM Transactions on Graphics, 2005

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5. Render Local and Synthetic Scene

Using light-based model as lighting

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6. Compositing

When estimate of local scene reflectance is accurate

Lfinal = Llocal+synthetic + (1) Lbackground

Lfinal

Lbackground

Llocal+synthetic

(Tone-mapped)

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6. Compositing using Differential Rendering

When estimate of local scene reflectance is not accurate

Lfinal = Llocal+synthetic + (1) (Lbackground + Llocal+synthetic Llocal )

Llocal+synthetic

(Tone-mapped)

Llocal (Tone-mapped)

Lfinal

45

Other Examples

46

Other Examples

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IBL References

Books and Notes

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting by Erik Reinhard, Greg Ward, Sumanta Pattanaik, and Paul Debevec, 2005

HDRI and Image-Based Lighting by Paul Devebec et al., SIGGRAPH 2003 Course #19, http://www.debevec.org/IBL2003/

Software Tools

HDR Shop: http://gl.ict.usc.edu/HDRShop/

Renderers

As listed in the global illumination references

Papers

[Devebec1998]

Paul Devebec, “Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography," SIGGRAPH '98

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Semi-Automatic Approach

Semi-Automatic Approach

From a single LDR photo, semi-automatically estimate

Geometry

Camera parameters

Surface properties

Lighting info

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System Overview

Scene synthesis

Object insertion Input image

Scene authoring

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System Overview

Scene authoring

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Bounding geometry

Occluding geometry

Supporting geometry

Light sources Spectral

matting[Levin et

al. ’09]

Manual input

Manual input

Spatial Layout

[Hedau et al. ’09]

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Textured billboard

(with transparency) Bounding cuboid

Extruded polygon

Area lights

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System Overview

Scene synthesis

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Scene Synthesis

Textured billboard Bounding

cuboid

Extruded polygon

Area lights

Physical scene model Rendered scene

Auto-material estimation

&

Auto-lighting refinement

Match input image and rendered scene 55

Material Estimation

Input + geometry

Direct

Reflectance

Retinex-like

decomposition

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Input image Physical model

Geometry w/

materials

Lights

Lighting Estimation

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Input image Rendered (initial)

Lighting Estimation

Rendered (final)

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Lighting Estimation

Result using initial

lights

Result using refined

lights

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External Lighting

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Shaft bounding box

Source bounding box

External Lighting

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Shaft direction

External Lighting

Shadow matting via [Guo et al. ‘11]

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System Overview

Object insertion

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Inserting Objects

Load scene into 3D modeler

Insert objects, animations

Render with any physically based renderer

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Final Composite

Additive differential technique [Debevec ‘98]

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Results

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References

Kevin Karsch, Varsha Hedau, David Forsyth, Derek Hoiem, "Rendering Synthetic Objects into Legacy Photographs," SIGGRAPH Asia 2011

http://kevinkarsch.com/publications/sa11.html

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The End