R I T Rochester Institute of Technology Photon Mapping Development LEO-15 DATA SETS (AVIRIS, IOPs) Atmospheric Compensation HYDROLIGHT Simulations (LUT)

R I T Rochester Institute of Technology

Photon Mapping Photon Mapping DevelopmentDevelopment

LEO-15 DATA SETS

(AVIRIS, IOPs)

Atmospheric Compensation

HYDROLIGHT Simulations

(LUT)

Spectral Matching

CONCENTRATION MAPS

GOALSGOALS

Summer 2003Summer 2003

PHOTON MAPPING

DEVELOPMENT

PHOTON MAPPING

Validation & Verification

CONESUS EXPERIMENT

Target Scenario,

Illumination, IOPs

HYDROLIGHT Simulations

Deep Water Scenarios

Large Scale

Shallow Water

Scenarios

Small Scale

CONCENTRATION MAPS

CONCENTRATION MAPS

BOTTOM TYPE MAPS

BATHYMETRY MAPS

ALGORITHM TRAINING/TESTIN

G


Water ModelWater Model

• Hydrolight works well for open ocean

cases

• Littoral environment does not fit

assumptions

Monte Carlo approach being

implemented


Basic Hydrolight WorldBasic Hydrolight World

Flat, constant bottom type

Detector

Random Surface(Spatially uncorrelated)

Slabs of homogeneous optical properties

MODTRAN Generated Sky

Output is a single point


A More Complex WorldA More Complex World

Variable, rough bottom types

Detector

Surface with spatial structure

Continuous/Arbitrary distribution of optical properties

MODTRAN Generated Sky

Object interaction

Output is a full scene

Underwater Plumes


Monte Carlo ApproachMonte Carlo Approach

•Arbitrary 3-dimensional structure can be

handled using a Monte Carlo based approach

•Monte Carlo techniques are generally useful

for very specific problems

•General Monte Carlo based solutions are

avoided because they are very inefficient

•We are expanding on a CG technique called

“Photon Mapping” (Jensen 2001) which speeds

up the calculation of indirect illumination

terms


A Simplified SceneA Simplified Scene

Light is incident on the surface of the water

Transmitted light is attenuated in the water

Scattered and reflected light returns to the surface

Light reaches the detector

Source Detector


Forward SimulationForward Simulation(Simple Monte Carlo Ray Tracing)(Simple Monte Carlo Ray Tracing)

Rays are traced from a light source

Light is randomly absorbed/scattered based on IOPs

Few rays make it to the water surface

(Most don’t even have a possibility of hitting the detector)

DetectorEven fewer make it to the detector

Source


Backward SimulationBackward Simulation(Based on Photon Reciprocity)(Based on Photon Reciprocity)

Rays are traced from the detector

Rays are randomly propagated until they hit a light source

The number of ray traces increases exponentially with the order of multiple scattering

Many directions are sampled at each event

DetectorSource


CompromiseCompromise(Two-Pass Solution )(Two-Pass Solution )

2nd Pass: Rays are traced from the detector

1st Pass: Rays are traced from light sources

Photon Map: A searchable database that stores the state of the in-water light field

Once populated, the photon map can be reused by every trace through the water

DetectorSource


Photon Map ConstructionPhoton Map Construction(1(1stst Pass – Pre-Processing Step) Pass – Pre-Processing Step)

Rays are traced from a light source

Light is randomly absorbed/scattered based on IOPs

At every absorption/ scattering event, a “photon” is stored in the map (location and direction)

1

1411 7

1215

2

3 6 10 9 5 4 13 16

8

Each photon is stored in a K-D binary tree (for quick searches) based on location

Source


Photon Map UsagePhoton Map Usage(2(2ndnd Pass – Image Construction Step) Pass – Image Construction Step)

Rays are traced from the detector

Rays are propagated directly until they hit a light source

The photon map is searched and the surrounding light field information is used to estimate the in-scattered radiance

DetectorSource


Example: Underwater SceneExample: Underwater Scene

Jensen

Lensing Effects

Scattering


MURI Water Model MURI Water Model CompositionComposition

•Spectral Information– Full spectral treatment corresponding to detector

sensitivity

•Measured/Modeled IOPs– Use the same inputs as Hydrolight

•Variance Reducing Sampling Techniques– Faster convergence to correct values

•Modeled Wave Surface– Generated from wave spectrum data

•Modularization– Use of in-house ray tracer and sensor testing environment


Spectral ConsiderationsSpectral Considerations

• Photon structure is condensed

• Number of photons used is

still very limited by memory

• Currently using a spectral

density technique

Position (12 bytes)Position (12 bytes)

Direction (2 bytes)Direction (2 bytes)

KDTree KDTree Flag andFlag andSpectral Info (2 bytes)Spectral Info (2 bytes)

16 b

ytes

16 b

ytes

Single wavelengthSingle wavelength

33,554,432 Photons

536,870,912 Bytes536,870,912 Bytes

1000 wavelengths1000 wavelengths

33,554 Photons/Wavelength


Sampling ConsiderationsSampling Considerations

• The majority of calculations in the model involve

random uniform samples on 2-manifolds

• Uniform pseudo-random points have a very slow

error reduction rate (slow convergence)

SUNSUN

Illumination Distribution

Area Calculation

Phase Functions


ACM SIGGRAPH 2003ACM SIGGRAPH 2003• Push to move towards stratified and quasi-

Monte Carlo sampling in CG community

• Allows for the error estimate to improve faster than a rate of 1/SQRT(N). (e.g. log(N)d/N)


Hybrid Sampling AlgorithmHybrid Sampling Algorithm

• Combination of Stratified and Latin Hypercube algorithms

• Guarantees uniformity without aliasing artifacts

Each cell contains one sample (Stratified)

Each row/column pair contains one sample (LHC)

Projection on 2-Manifolds


Sampling: Integration of 2D Sampling: Integration of 2D SINC FunctionSINC Function

• 5000 runs of 25, 625, and 15625 samples of a 2D

SINC function (continuous band of frequencies)

• Hybrid algorithm converges faster and produces

less outliers (Gaussian shaped)

2D Random Sampling 2D Hybrid Sampling

0.2000.200 0.2010.201 0.0800.080 0.0410.041


Wave Model IntegrationWave Model Integration

=

1D Frequency spectrumDirectional distribution 2D Frequency spectrum

Frequency [Hz]

U19.5 = 17.5 m/s

U19.5 = 15 m/s

U19.5 = 12.5 m/sU19.5 = 10 m/s

S

[m2 s

]

Frequency [Hz]

U19.5 = 17.5 m/s

U19.5 = 15 m/s

U19.5 = 12.5 m/sU19.5 = 10 m/s

S

[m2 s

]

Parameterized Wave ModelOr Measured

Wave Spectrum

FT


Modularization Using Generic Modularization Using Generic InterfacesInterfaces

RadiometrySolvers

DIRSIG (Detectors, 3D Models, etc.)

WaterModel

IOP Server

Sample Generator

Photon Map

Air/Water Interface

Phase Functions


Photon Map Construction Photon Map Construction and Searches in Paralleland Searches in Parallel

• Many computers can construct photon maps independently to form a larger collective map

• A single search query by radius can integrate contributions from each independent map


Current ProgressCurrent Progress

•Majority of routines are in place within modular structure

•Currently working on issues related to sampling

•Preliminary validation projected for Fall/Winter 2003


Concluding RemarksConcluding Remarks

Development Goals• Provide a complex testing environment for

target detection algorithms

• Allow for continuous improvement through

a modular interface

• Provide generic tools that are able to solve

new problems without internal modification

• Allow for automated generation of LUTs and

target subspaces


Questions?Questions?

Documents

R I T Rochester Institute of Technology Photon Mapping Development LEO-15 DATA SETS (AVIRIS, IOPs) Atmospheric Compensation HYDROLIGHT Simulations (LUT)