Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used brute-force techniques that require on the order of 100 seconds to render typical data sets on a workstation. Algorithms with optimizations that exploit coherence in the data have reduced rendering times to the range of ten seconds but are still not fast enough for interactive visualization applications. In this thesis we present a family of volume rendering algorithms that reduces rendering times to one second. First we present a scanline-order volume rendering algorithm that exploits coherence in both the volume data and the image. We show that scanline-order algorithms are fundamentally more efficient than commonly-used ray casting algorithms because the latter must perform analytic geometry calculations (e.g. intersecting rays with axis-aligned boxes). The new scanline-order algorithm simply streams through the volume and the image in storage order. We describe variants of the algorithm for both parallel and perspective projections and

This paper presents a two pass global illumination method based on the concept of photon maps. It represents a significant improvement of a previously described approach both with respect to speed, accuracy and versatility. In the first pass two photon maps are created by emitting packets of energy (photons) from the light sources and storing these as they hit surfaces within the scene. We use one high resolution caustics photon map to render caustics that are visualized directly and one low resolution photon map that is used during the rendering step. The scene is rendered using a distribution ray tracing algorithm optimized by using the information in the photon maps. Shadow photons are used to render shadows more efficiently and the directional information in the photon map is used to generate optimized sampling directions and to limit the recursion in the distribution ray tracer by providing an estimate of the radiance on all surfaces with the exception of specular...

We present a fundamental procedure for instant rendering from the radiance equation. Operating directly on the textured scene description, the very efficient and simple algorithm produces photorealistic images without any finite element kernel or solution discretization of the underlying integral equation. Rendering rates of a few seconds are obtained by exploiting graphics hardware, the deterministic technique of the quasi-random walk for the solution of the global illumination problem, and the new method of jittered low discrepancy sampling.

We present a new Monte Carlo method for solving the light transport problem, inspired by the Metropolis sampling method in computational physics. To render an image, we generate a sequence of light transport paths by randomly mutating a single current path (e.g. adding a new vertex to the path). Each mutation is accepted or rejected with a carefully chosen probability, to ensure that paths are sampled according to the contribution they make to the ideal image. We then estimate this image by sampling many paths, and recording their locations on the image plane. Our algorithm is unbiased, handles general geometric and scattering models, uses little storage, and can be orders of magnitude more e#cient than previous unbiased approaches. It performs especially well on problems that are usually considered di#cult, e.g. those involving bright indirect light, small geometric holes, or glossy surfaces. Furthermore, it is competitive with previous unbiased algorithms even for relatively simple ...

Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods. In these cases variance can often be significantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Normally this is done by explicitly partitioning the integration domain into regions that are sampled differently. We present a powerful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is provably good. These estimators are unbiased, and can reduce variance significantly at little additional cost. We present experiments and measurements from several areas in rendering: calculation of glossy highlights from area light sources, the “final gather” pass of some radiosity algorithms, and direct solution of the rendering equation using bidirectional path tracing.

We examine various simple algorithms that exploit homogeneity and accumulated opacity for tracing rays through shaded volumes. Most of these methods have error criteria which allow them to trade quality for speed. The time vs. quality tradeoff for these adaptive methods is compared to fixed step multiresolution methods. These methods are also useful for general light transport in volumes. 1 Introduction We are interested in speeding volume ray tracing computations. We concentrate on the one dimensional problem of tracing a single ray, or computing the intensity at a point from a single direction. In addition to being the kernel of a simple volume ray tracer, this computation can be used to generate shadow volumes and as an element in more general light transport problems. Our data structures will be view independent to speed the production of animations of preshaded volumes and interactive viewing. In [11] Levoy introduced two key concepts which we will be expanding on: presence accel...

Creating realistic images has been a major focus in the study of computer graphics for much of its history. This e ort has led to mathematical models and algorithms that can compute predictive, or physically realistic, images from known camera positions and scene descriptions that include the geometry of objects, the re ectance of surfaces, and the lighting used to illuminate the scene. These images accurately describe the physical quantities that would be measured from a real scene. Because these algorithms can predict real images, they can also be used in inverse problems to work backward from photographs to attributes of the scene. Work on three such inverse rendering problems is described. The rst, inverse lighting, assumes knowledge of geometry, re ectance, and the recorded photograph and solves for the lighting in the scene. A technique using a linear least-squares system is proposed and demonstrated. Also demonstrated is an application of inverse lighting, called re-lighting, which modi es lighting in photographs. The second two inverse rendering problems solve for unknown re ectance, given images with known geometry, lighting, and camera positions. Photographic texture measurement

Realistic image generation is presented in a theoretical formulation that builds from previous work on the rendering equation. Previous and new solution techniques for the global illumination are discussed in the context of this formulation. The basic

: This paper presents a new importance sampling strategy for Monte Carlo ray tracing in which a rough estimate of the irradiance based on the photon map is combined with the local reflection model to construct more efficient probability density functions that can be used in an importance sampling scheme. The algorithm gives unbiased results, handles arbitrary reflection models and it is particularly efficient in scenes with highly non-uniform indirect illumination. Initial results and comparisons with traditional importance sampling strategies indicate a reduction in the noise level of more than 70% Key Words: Global Illumination, Path Tracing, Importance Sampling, Photon Map 1 Introduction Photorealistic rendering requires accurate simulation of global illumination and much work has been done in this area in the last 10 years. The problem was actually solved in 1986 by Kajiya [6] using a method called path tracing. This method is basically a brute force Monte Carlo simulat...

Abstract not found