TVCG Invited Partnership Presentations

Materializing Inter-Channel Relationships with Multi-Density Woodcock Tracking

Alper Sahistan (SCI Institute), Stefan Zellmann (University of Cologne), Haichao Miao (Lawrence Livermore National Laboratory), Nate Morrical, Ingo Wald (NVIDIA), Valerio Pascucci (Scientific Computing and Imaging Institute, University of Utah)

Rendering Computer GraphicsImage Color AnalysisTransfer FunctionsMonte Carlo MethodsMathematical ModelsData VisualizationPhotonicsCostsThree Dimensional DisplaysStandardsRay TracingVolume RenderingScientific VisualizationMonte Carlo MethodsMonte Carlo SimulationComputer ScienceTransfer FunctionMaximum DensityMultiple ChannelsMaximum Intensity ProjectionColor MapScalar ValueDepth PerceptionRay TracingN ChannelLinear CostVolume RenderingVisual ClutterNyquist RateChannel Transfer3 D TextureScientific VisualizationUniversity Of UtahDomain ExpertsPerformance PenaltyColor MixingAdaptive SamplingSerializedFluid Dynamics

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Presentation

Session
Turn up the volume(s)!
Time
Thursday, Nov 12, 14:00 – 14:12 (US/Eastern) · session 13:00 – 14:30
Room
Hall America north

Abstract

Volume rendering techniques for scientific visualization has recently shifted toward Monte Carlo (MC) methods for their flexibility and robustness, but their use in multi-channel visualization remains underexplored. Traditional multi-channel volume rendering often relies on arbitrary, non-physically-based color blending functions that hinder interpretation. We introduce multi-density Woodcock tracking, a simple extension of Woodcock tracking that leverages an MC method to produce high-fidelity, physically grounded multi-channel renderings without arbitrary blending. By generalizing Woodcock's distance tracking, we provide a unified blending modality that also integrates blending functions from prior works. We further implement effects that enhance boundary and feature recognition. By accumulating frames in real-time, our approach delivers high-quality visualizations with perceptual benefits, demonstrated on diverse datasets.