R I T Rochester Institute of Technology Photon Mapping Development LEO-15 DATA SETS (AVIRIS, IOPs) Atmospheric Compensation HYDROLIGHT Simulations (LUT) Spectral Matching CONCENTRATION MAPS GOALS Summer 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 BOTTOM TYPE MAPS BATHYMETRY MAPS ALGORITHM TRAINING/TESTING

R I T Rochester Institute of Technology Water Model Hydrolight works well for open ocean cases Littoral environment does not fit assumptions  Monte Carlo approach being implemented

R I T Rochester Institute of Technology Basic Hydrolight World Flat, constant bottom type Detector Random Surface (Spatially uncorrelated) Slabs of homogeneous optical properties MODTRAN Generated Sky Output is a single point

R I T Rochester Institute of Technology A 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

R I T Rochester Institute of Technology Monte 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

R I T Rochester Institute of Technology A 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 SourceDetector

R I T Rochester Institute of Technology Forward Simulation (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) Detector Even fewer make it to the detector Source

R I T Rochester Institute of Technology Backward Simulation (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

R I T Rochester Institute of Technology Compromise (Two-Pass Solution ) 2 nd Pass: Rays are traced from the detector 1 st 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

R I T Rochester Institute of Technology Photon Map Construction (1 st 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) Each photon is stored in a K-D binary tree (for quick searches) based on location Source

R I T Rochester Institute of Technology Photon Map Usage (2 nd 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

R I T Rochester Institute of Technology Example: Underwater Scene Jensen Lensing Effects Scattering

R I T Rochester Institute of Technology MURI Water Model Composition 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

R I T Rochester Institute of Technology Spectral Considerations Photon structure is condensed Number of photons used is still very limited by memory Currently using a spectral density technique Single wavelength 33,554,432 Photons 536,870,912 Bytes 1000 wavelengths 33,554 Photons/Wavelength

R I T Rochester Institute of Technology Sampling 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) SUN Illumination Distribution Area Calculation Phase Functions

R I T Rochester Institute of Technology ACM 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)

R I T Rochester Institute of Technology Hybrid 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

R I T Rochester Institute of Technology Sampling: Integration of 2D SINC Function 5000 runs of 25, 625, and samples of a 2D SINC function (continuous band of frequencies) Hybrid algorithm converges faster and produces less outliers (Gaussian shaped) 2D Random Sampling2D Hybrid Sampling 0.20     1

R I T Rochester Institute of Technology Wave Model Integration = 1D Frequency spectrum Directional distribution 2D Frequency spectrum Parameterized Wave Model Or Measured Wave Spectrum FT

R I T Rochester Institute of Technology Modularization Using Generic Interfaces Radiometry Solvers DIRSIG (Detectors, 3D Models, etc.) Water Model IOP Server Sample Generator Photon Map Air/Water Interface Phase Functions

R I T Rochester Institute of Technology Photon Map Construction and 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

R I T Rochester Institute of Technology Current Progress Majority of routines are in place within modular structure Currently working on issues related to sampling Preliminary validation projected for Fall/Winter 2003

R I T Rochester Institute of Technology Concluding 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

R I T Rochester Institute of Technology Questions?