Abstract In recent work, Nanite has demonstrated how to rasterize virtualized micro‐poly geometry in real time, thus enabling immense geometric complexity. We present a system that employs similar methods for real‐time ray tracing of micro‐poly geometry. The geometry is preprocessed in almost the same fashion: Nearby triangles are clustered together and clusters get merged and simplified to obtain hierarchical level of detail (LOD). Then these clusters are compressed and stored in a GPU‐friendly data structure. At run time, Nanite selects relevant clusters, decompresses them and immediately rasterizes them. Instead of rasterization, we decompress each selected cluster into a small bounding volume hierarchy (BVH) in the format expected by the ray tracing hardware. Then we build a complete BVH on top of the bounding volumes of these clusters and use it for ray tracing. Our BVH build reaches more than 74% of the attainable peak memory bandwidth and thus it can be done per frame. Since LOD selection happens per frame at the granularity of clusters, all triangles cover a small area in screen space.

We present a novel image-based representation to interactively visualize large and arbitrarily structured volumetric data. This image-based representation is created from a fixed view and models the scalar densities along each viewing ray. Then, any transfer function can be applied and changed interactively to visualize the data. In detail, we transform the density in each pixel to the Fourier basis and store Fourier coefficients of a bounded signal, i.e. bounded trigonometric moments. To keep this image-based representation compact, we adaptively determine the number of moments in each pixel and present a novel coding and quantization strategy. Additionally, we perform spatial and temporal interpolation of our image representation and discuss the visualization of introduced uncertainties. Moreover, we use our representation to add single scattering illumination. Lastly, we achieve accurate results even with changes in the view configuration. We evaluate our approach on two large volume datasets and a time-dependent SPH dataset.

Abstract We introduce an efficient method to sample linear lights, i.e. infinitesimally thin cylinders, proportional to projected solid angle. Our method uses inverse function sampling with a specialized iterative procedure that converges to high accuracy in only two iterations. It also allows us to sample proportional to a linearly transformed cosine. By combining both sampling techniques through suitable multiple importance sampling heuristics and by using good stratification, we achieve unbiased diffuse and specular real‐time shading with low variance outside penumbrae at two samples per pixel. Additionally, we provide a fast method for solid angle sampling.

With the advent of real-time ray tracing, there is an increasing interest in GPU-friendly importance sampling techniques. We present such methods to sample convex polygonal lights approximately proportional to diffuse and specular BRDFs times the cosine term. For diffuse surfaces, we sample the polygons proportional to projected solid angle. Our algorithm partitions the polygon suitably and employs inverse function sampling for each part. Inversion of the distribution function is challenging. Using algebraic geometry, we develop a special iterative procedure and an initialization scheme. Together, they achieve high accuracy in all possible situations with only two iterations. Our implementation is numerically stable and fast. For specular BRDFs, this method enables us to sample the polygon proportional to a linearly transformed cosine. We combine these diffuse and specular sampling strategies through novel variants of optimal multiple importance sampling. Our techniques render direct lighting from Lambertian polygonal lights with almost no variance outside of penumbrae and support shadows and textured emission. Additionally, we propose an algorithm for solid angle sampling of polygons. It is faster and more stable than existing methods.

Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach.

We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm.

We present a compact and efficient representation of spectra for accurate rendering using more than three dimensions. While tristimulus color spaces are sufficient for color display, a spectral renderer has to simulate light transport per wavelength. Consequently, emission spectra and surface albedos need to be known at each wavelength. It is practical to store dense samples for emission spectra but for albedo textures, the memory requirements of this approach are unreasonable. Prior works that approximate dense spectra from tristimulus data introduce strong errors under illuminants with sharp peaks and in indirect illumination. We represent spectra by an arbitrary number of Fourier coefficients. However, we do not use a common truncated Fourier series because its ringing could lead to albedos below zero or above one. Instead, we present a novel approach for reconstruction of bounded densities based on the theory of moments. The core of our technique is our bounded maximum entropy spectral estimate. It uses an efficient closed form to compute a smooth signal between zero and one that matches the given Fourier coefficients exactly. Still, a ground truth that localizes all of its mass around a few wavelengths can be reconstructed adequately. Therefore, our representation covers the full gamut of valid reflectances. The resulting textures are compact because each coefficient can be stored in 10 bits. For compatibility with existing tristimulus assets, we implement a mapping from tristimulus color spaces to three Fourier coefficients. Using three coefficients, our technique gives state of the art results without some of the drawbacks of related work. With four to eight coefficients, our representation is superior to all existing representations. Our focus is on offline rendering but we also demonstrate that the technique is fast enough for real-time rendering.

Building upon previous works, we transfer the recently proposed moment shadow mapping to three new applications. Like variance shadow maps and convolution shadow maps, moment shadow maps can be filtered directly. Classically, this is used to filter hard shadows but previous works explore other applications. Prefiltered single scattering uses convolution shadow maps to render single scattering in homogenous participating media, variance soft shadow mapping uses variance shadow maps for approximate soft shadows and Fourier opacity mapping uses convolution shadow maps for translucent occluders. We combine these three techniques with moment shadow mapping to arrive at better heuristics with less computational overhead.

We present moment shadow mapping, a novel technique for fast, filtered hard shadows. Like variance shadow mapping it allows for the application of all kinds of efficient texture filtering and antialiasing to its moment shadow map. However it is designed to provide a substantially higher quality. Moment shadow maps store four moments of the depth within the filter kernel. Using this information, our efficient algorithm computes the sharpest possible lower bound as approximation to the shadow intensity. The choice to compute such a bound using four moments is based upon an automated evaluation of thousands of alternatives and thus known to be optimal. To reduce memory and bandwidth requirements we present an optimized quantization scheme to allow 16-bit quantization of moment shadow maps. Our evaluation demonstrates that moment shadow mapping produces high quality results with a single shadow map sample per fragment using 64 bits per shadow map texel.

Transient images help to analyze light transport in scenes. Besides two spatial dimensions, they are resolved in time of flight. Cost-efficient approaches for their capture use amplitude modulated continuous wave lidar systems but typically take more than a minute of capture time. We propose new techniques for measurement and reconstruction of transient images, which drastically reduce this capture time. To this end, we pose the problem of reconstruction as a trigonometric moment problem. A vast body of mathematical literature provides powerful solutions to such problems. In particular, the maximum entropy spectral estimate and the Pisarenko estimate provide two closed-form solutions for reconstruction using continuous densities or sparse distributions, respectively. Both methods can separate m distinct returns using measurements at m modulation frequencies. For m = 3 our experiments with measured data confirm this. Our GPU-accelerated implementation can reconstruct more than 100000 frames of a transient image per second. Additionally, we propose modifications of the capture routine to achieve the required sinusoidal modulation without increasing the capture time. This allows us to capture up to 18.6 transient images per second, leading to transient video. An important byproduct is a method for removal of multipath interference in range imaging.