Correlated Photon Mapping for Interactive Global Illumination of Time-Varying Volumetric Data

Abstract

Abstract-We present a method for interactive global illumination of both static and time-varying volumetric data based on reduction of the overhead associated with re-computation of photon maps. Our method uses the identification of photon traces invariant to changes of visual parameters such as the transfer function (TF), or data changes between time-steps in a 4D volume. This lets us operate on a variant subset of the entire photon distribution. The amount of computation required in the two stages of the photon mapping process, namely tracing and gathering, can thus be reduced to the subset that are affected by a data or visual parameter change. We rely on two different types of information from the original data to identify the regions that have changed. A low resolution uniform grid containing the minimum and maximum data values of the original data is derived for each time step. Similarly, for two consecutive time-steps, a low resolution grid containing the difference between the overlapping data is used. We show that this compact metadata can be combined with the transfer function to identify the regions that have changed. Each photon traverses the low-resolution grid to identify if it can be directly transferred to the next photon distribution state or if it needs to be recomputed. An efficient representation of the photon distribution is presented leading to an order of magnitude improved performance of the raycasting step. The utility of the method is demonstrated in several examples that show visual fidelity, as well as performance. The examples show that visual quality can be retained when the fraction of retraced photons is as low as 40%-50%.

Real-Time Denoising of Volumetric Path Tracing for Direct Volume Rendering

“…While this method allows real-time radiance reconstruction, the photon map should be regenerated every time the transfer function changes. To accelerate the photon map generation, Jönsson et al [24] identified photons invariant to changes of visual parameters (e.g., changes in the transfer function), enabling a further reduction of the overhead associated with recomputing photon maps. Unlike MC path tracing, photon mapping typically introduces some bias, often visible as low-frequency noise.…”

Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions

“…The presented methods have been integrated into an OpenCL-based path tracer in the Inviwo [10] visualization framework running on both the CPU and the GPU. The path tracer implementation utilizes partitioned tracking through a super-grid (8 3 voxels per grid cell) storing the extinction bounds and average of each grid cell, see [16] and [11] for more details. An Intel Xeon 2.5 GHz CPU with 32 GB random access memory and a Geforce 1070 GPU is uses as testing hardware.…”

VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data

“…[NEO*10] demonstrated a real‐time pipeline for analysing the 3D fMRI signal during the scanning process, using an approximated method for treating the fMRI signal as an emissive light source and fusing it with a co‐registered magnetic resonance imaging (MRI) scan [HLY07]. Later it has also been shown how to more accurately simulate this emissive light transport interactively in brain imaging data [JY17]. These methods, fusing fMRI and MRI data using light transport, form the basis for the 3D views in the herein presented work and we refer the reader to the multi‐modal medical visualization data survey by Lawonn et al .…”