No abstract

We present a novel method to optimize the attenuation of light for the single scattering model in direct volume rendering. A common problem of single scattering is the high dynamic range between lit and shadowed regions due to the exponential attenuation of light along a ray. Moreover, light is often attenuated too strong between a sample point and the camera, hampering the visibility of important features. Our algorithm employs an importance function to selectively illuminate important structures and make them visible from the camera. With the importance function, more light can be transmitted to the features of interest, while contextual structures cast shadows which provide visual cues for perception of depth. At the same time, more scattered light is transmitted from the sample point to the camera to improve the primary visibility of important features. We formulate a minimization problem that automatically determines the extinction along a view or shadow ray to obtain a good balance between sufficient transmittance and attenuation. In contrast to previous approaches, we do not require a computationally expensive solution of a global optimization, but instead provide a closed-form solution for each sampled extinction value along a view or shadow ray and thus achieve interactive performance.

Samples with high contribution but low probability density, often called fireflies, occur in all practical Monte Carlo estimators and are part of computing unbiased estimates. For finite‐sample estimates, however, they can lead to excessive variance. Rejecting all samples classified as outliers, as suggested in previous work, leads to estimates that are too low and can cause undesirable artefacts. In this paper, we show how samples can be re‐weighted depending on their contribution and sampling frequency such that the finite‐sample estimate gets closer to the correct expected value and the variance can be controlled. For this, we first derive a theory for how samples should ideally be re‐weighted and that this would require the probability density function of the optimal sampling strategy. As this probability density function is generally unknown, we show how the discrepancy between the optimal and the actual sampling strategy can be estimated and used for re‐weighting in practice. We describe an efficient algorithm that allows for the necessary analysis of per‐pixel sample distributions in the context of Monte Carlo rendering without storing any individual samples, with only minimal changes to the rendering algorithm. It causes negligible runtime overhead, works in constant memory and is well suited for parallel and progressive rendering. The re‐weighting runs as a fast post‐process, can be controlled interactively and our approach is non‐destructive in that the unbiased result can be reconstructed at any time.

In this paper we present a novel GPU-friendly real-time voxelization technique for rendering homogeneous media that is defined by particles, e.g., fluids obtained from particle-based simulations such as Smoothed Particle Hydrodynamics (SPH). Our method computes view-adaptive binary voxelizations with on-the-fly compression of a tiled perspective voxel grid, achieving higher resolutions than previous approaches. It allows for interactive generation of realistic images, enabling advanced rendering techniques such as ray casting-based refraction and reflection, light scattering and absorption, and ambient occlusion. In contrast to previous methods, it does not rely on preprocessing such as expensive, and often coarse, scalar field conversion or mesh generation steps. Our method directly takes unsorted particle data as input. It can be further accelerated by identifying fully populated simulation cells during simulation. The extracted surface can be filtered to achieve smooth surface appearance. Finally, we provide a new scheme for accelerated ray casting inside the voxelization.