r i t rochester institute of technology photon mapping development leo-15 data sets (aviris, iops)...
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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
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Water ModelWater Model
• Hydrolight works well for open ocean
cases
• Littoral environment does not fit
assumptions
Monte Carlo approach being
implemented
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Example: Underwater SceneExample: Underwater Scene
Jensen
Lensing Effects
Scattering
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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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
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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
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Questions?Questions?