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The Analysis of Global Illumination Rendering Based on BRDF
Abstract
The Bi-directional Reflectance Distribution Function (BRDF) describes the appearance of a material by its interaction with light at a surface point. When the materials in scene are invariant, a linear transformation occurs in the transmission change from lighting to outgoing radiance, which makes a real-time global illumination rendering achieved. However, such linear transformation does not hold when materials change. This paper presents a real-time rendering algorithm for scenes with editable materials under direct and indirect illumination. All materials in scenes are represented as various linear combinations of the BRDF base, from which, all outgoing radiance luminance of each combination are pre-computed. When it renders, linear combines the pre-computed data by the coefficient resulted from different materials projection on the base, the nonlinear problem is converted to a linear one, and the final effects of global illumination rendering in real-time can be achieved.
A new algorithm is proposed to render the earth with atmospheric effects, in which the light intensity is analyzed and divided into three parts that can be calculated individually. Satellite remote sensing data is used to be inverted to ground bidirectional reflectance model. Texture of surface reflectance, optical depth, emissivity and temperature is generated in the phase of data processing, which is applied to GPU-based real-time rendering system. Thus light interacting effect between ground and atmosphere is realized. The method is proved to render the scene more realistic and immersive, and shows high efficiency and flexibility for complex application.
The rendering equation is similar to the linear Boltzmann
equation which has been widely studied in physics and nuclear engineering. Consequently, many of the powerful techniques which have been developed in these fields can be
applied to problems in image synthesis. In this paper we
adapt several statistical techniques commonly used in neutron transport to stochastic ray tracing and, more generally, to Monte Carlo solution of the rendering equation. First, we describe a technique known as Russian roulette which can be used to terminate the recursive tracing of rays without introducing statistical bias. We also examine the practice of creating ray trees in classical ray tracing in the light of a
well-known technique in particle transport known as splitting. We show that neither ray trees nor paths as described in [10] constitute an optimal sampling plan in themselves and that a hybrid may be more efficient.
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 signifi-cantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Nor-mally this is done by explicitly partitioning the integration domain into regions that are sampled differently. We present a power-ful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is prov-ably 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 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 ...
In this paper we present a new Monte Carlo rendering algorithm that seamlessly integrates
We present a soft shadow technique for dynamic scenes with moving objects under the combined illumination of moving local light sources and dynamic environment maps. The main idea of our technique is to precompute for each scene entity a shadow field that describes the shadowing effects of the entity at points around it. The shadow field for a light source, called a source radiance field (SRF), records radiance from an illuminant as cube maps at sampled points in its surrounding space. For an occluder, an object occlusion field (OOF) conversely represents in a similar manner the occlusion of radiance by an object. A fundamental difference between shadow fields and previous shadow computation concepts is that shadow fields can be precomputed independent of scene configuration. This is critical for dynamic scenes because, at any given instant, the shadow information at any receiver point can be rapidly computed as a simple combination of SRFs and OOFs according to the current scene configuration. Applications that particularly benefit from this technique include large dynamic scenes in which many instances of an entity can share a single shadow field. Our technique enables low-frequency shadowing effects in dynamic scenes in real-time and all-frequency shadows at interactive rates.
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%