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We present several formulas and methods for generating samples distributed according to a desired probability density function on a specific domain. Sampling is a fundamental operation in modern rendering, both at runtime and in preprocessing. It is becoming ever more prevalent with the introduction of ray tracing in standard APIs, as many ray tracing algorithms are based on sampling by nature. This chapter provides a concise list of some useful tricks and methods.

The introduction of standardized APIs for ray tracing, together with hardware acceleration, opens up possibilities for physically based lighting in real-time rendering. Light importance sampling is one of the fundamental operations in light transport simulations, applicable to both direct and indirect illumination. This chapter describes a bounding volume hierarchy data structure and associated sampling methods to accelerate importance sampling of local light sources. The work is based on recently published methods for light sampling in production rendering, but it is evaluated in a real-time implementation using Microsoft DirectX Raytracing.

Stereoscopic rendering and 3D stereo displays are quickly becoming mainstream. The natural extension is autostereoscopic multi-view displays, which by the use of parallax barriers or lenticular lenses, can accommodate many simultaneous viewers without the need for active or passive glasses. As these displays, for the foreseeable future, will support only a rather limited number of views, there is a need for high-quality interperspective antialiasing. We present a specialized algorithm for efficient multi-view image generation from a camera line using ray tracing, which builds on previous methods for multi-dimensional adaptive sampling and reconstruction of light elds. We introduce multi-view silhouette edges to detect sharp geometrical discontinuities in the radiance function. These are used to significantly improve the quality of the reconstruction. In addition, we exploit shader coherence by computing analytical visibility between shading points and the camera line, and by sharing shading computations over the camera line.

A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial detail and prevent motion blur. In color image data, the chromaticity is restored and demosaicing of raw RGB input data is performed simultaneously with the noise reduction. The method is very general, contains few user-defined parameters and has been developed for efficient parallel computation using a GPU. The technique has been applied to image sequences with various degrees of darkness and noise levels, and results from some of these tests, and comparisons to other methods, are presented. The present work has been inspired by research on vision in nocturnal animals, particularly the spatial and temporal visual summation that allows these animals to see in dim light.

In this paper, we break new ground by presenting algorithms for fixed-rate compression of high dynamic range textures at low bit rates. First, the S3TC low dynamic range texture compression scheme is extended in order to enable compression of HDR data. Second, we introduce a novel robust algorithm that offers superior image quality. Our algorithm can be efficiently implemented in hardware, and supports textures with a dynamic range of over 10 9 :1. At a fixed rate of 8 bits per pixel, we obtain results virtually indistinguishable from uncompressed HDR textures at 48 bits per pixel. Our research can have a big impact on graphics hardware and real-time rendering, since HDR texturing suddenly becomes affordable.

We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on the fly, based on hierarchical representations of the local maxima and averages of the individual terms. Samples are generated by exploiting the hierarchical properties of many low-discrepancy sequences, and thresholded against the estimated product. We evaluate direct illumination by sampling the triple product of environment map lighting, surface reflectance, and a visibility function estimated per pixel. Our results show considerable noise reduction compared to existing state-of-the-art methods using only the product of lighting and BRDF.