Global Illumination

2. Abstract Automatic 3D acquisition devices (often called 3D scanners) allow to build highly accurate models of real 3D objects in a cost-and time-effective manner. We have experimented this technology in a particular application context: the acquisition of Cultural Heritage artefacts. Specific needs of this domain are: medium-high accuracy, easy of use, affordable cost of the scanning device, self-registered acquisition of shape and color data, and finally operational safety for both the operator and the scanned artefacts. According to ...

Effective change detection under dynamic illumination conditions is an active research topic. Most research has concentrated on adaptive statistical representations for the appearance of the background scene. There is limited work that develops the statistical models for background representation by taking into account an explicit model for the camera response function, the camera noise model, and illumination priors. Assuming a monotone but non-linear camera response function, a Phong shading model for the surface material, and a locally constant but spatially varying illumination, we show that the sign of the difference between two pixel measurements is maintained across global illumination changes. We use this result along with a statistical model for the camera noise to develop a change detection algorithm that deals with sudden changes in illumination. The performance evaluation of the algorithm is done through simulations and on real data.

In this paper we propose a new physically plausible, anisotropic Bidirectional Reflectance Distribution Function (BRDF) for fitting and for importance sampling in global illumination rendering. We demonstrate that the new model is better in data fitting than existing BRDF models. We also describe efficient schemes for sampling the proposed anisotropic BRDF model. Furthermore, we test it on a GPU-based real-time rendering algorithm and show that material design can be done with this anisotropic BRDF model effectively. We also show that the new model has effective real-time rendering performance.

We introduce a novel voxel-based algorithm that interactively simulates both diffuse and glossy single-bounce indirect illumination. Our algorithm generates high quality images similar to the reference solution while using only a fraction of the memory of previous methods. The key idea in our work is to decouple occlusion data, stored in voxels, from lighting and geometric data, encoded in a new per-light data structure called layered reflective shadow maps (LRSMs). We use voxel cone tracing for visibility determination and integrate outgoing radiance by performing lookups in a pre-filtered LRSM. Finally we demonstrate that our simple data structures are easy to implement and can be rebuilt every frame to support both dynamic lights and scenes.

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MERGING IS SEX OBVIOUSLY,  NORMAL MERGING IS KNOWING THE PAST, PRESENT AND FUTURE OF BOTH BEINGS, NO YOU OR I = ONENESS,  THE TWO ANUs MERGE – IF ONE IS AN ATTACK, THEN THE ORIGINAL ANU BECOMES SMALLER, DUMBER AND WEAKER, WHICH IS WHAT IS GOING ON HERE. 

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GPU-based interactive global illumination techniques are receiving an increasing interest from both the research and the industrial community as real-time graphics applications strive for visually rich and realistic dynamic three-dimensional environments. This paper presents a fast new diffuse global illumination method that generates a sparse set of low-cost radiance field evaluation points (radiance hints) and computes an arbitrary number of diffuse inter-reflections within a given volume. The proposed approximate technique combines ideas from exiting grid-based radiance caching techniques with reflective shadow maps as well as a stochastic scheme for visibility calculations, in order to achieve high frame rates for multiple light bounces.

Generating photo-realistic images through Monte Carlo rendering requires efficient representation of light–surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four-dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well-known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.

This paper presents the fusion of monogenic signal process- ing and dierential geometry to enable monogenic analyzing of local intrinsic 2D features of low level image data. New rotational invariant features such like structure and geometry (angle of intersection) of two superimposed intrinsic 1D signals will be extracted without the need of any steerable filters. These features are important for