Concrete Computation of Global Illumination Using Structured Sampling

A new methodology is presented for the computation of global illumination using structured sampling. Analytical/numerical solutions for illumination are developed for simple lighting configurations. These solutions are subsequently used to generate accurate reference images. The structured sampling solution for global illumination is then discussed, comprising sample placement for illumination calculation, reconstruction for light transfer and finally resampling and filtering of illumination samples for display. A first approximation to this technique is presented using a priori placement of samples, irregular polygon reflectors, grid resampling and a conical filter for display. The new algorithm is evaluated for image quality, and compared to the traditional radiosity-based approach. These first results show that the structured sampling solution yields significant computational savings while maintaining high image quality. 1. Goals of the Approach The calculation of global illumination is inherently complex, even for environments that are simpl

Tightly-Coupled Multiprocessing for a Global Illumination Algorithm

{dret | elf} @ dgp.toronto.edu A prevailing trend in computer graphics is the demand for increasingly realistic global illumination models and algorithms. Despite the fact that the computational power of uniprocessors is increasing, it is clear that much greater computational power is required to achieve satisfactory throughput. The obvious next step is to employ parallel processing. The advent of affordable, tightly-coupled multiprocessors makes such an approach widely available for the first time. We propose a tightly-coupled parallel decomposition of FIAT, a global illumination algorithm, based on space subdivision and power balancing, that we have recently developed. This algorithm is somewhat ambitious, and severely strains existing uniprocessor environments. We discuss techniques for reducing memory contention and maximising parallelism. We also present empirical data on the actual performance of our parallel solution. Since the model of parallel computation that we have employed is likely to persist for quite some time, our techniques are applicable to other algorithms based on space subdivision. 1

A constructive theory of sampling for image synthesis using reproducing kernel bases

Sampling a scene by tracing rays and reconstructing an image from such pointwise samples is fundamental to computer graphics. To improve the efficacy of these computations, we propose an alternative theory of sampling. In contrast to traditional formulations for image synthesis, which appeal to nonconstructive Dirac deltas, our theory employs constructive reproducing kernels for the correspondence between continuous functions and pointwise samples. Conceptually, this allows us to obtain a common mathematical formulation of almost all existing numerical techniques for image synthesis. Practically, it enables novel sampling based numerical techniques designed for light transport that provide considerably improved performance per sample. We exemplify the practical benefits of our formulation with three applications: pointwise transport of color spectra, projection of the light energy density into spherical harmonics, and approximation of the shading equation from a photon map. Experimental results verify the utility of our sampling formulation, with lower numerical error rates and enhanced visual quality compared to existing techniques

Accurate, Consistent Reconstruction of Illumination Functions Using Structured Sampling

International audienceThe study of common classes of diffuse emitters, such as planar convex polygons, reveals several interesting properties of the functions of illumination these emitters cast on receiver surfaces. Some properties, such as the position of the maximum and the curvature are of particular interest for sampling and reconstruction of illumination across receivers. A computationally efficient approach is presented that identifies these properties, and uses them to select samples of illumination. In addition these properties are used to determine upper bounds on the error due to linear and quadratic interpolants. These bounds are then used to adaptively subdivide the nonuniform sampling grid, resulting in accurate reconstruction. Results show that the method reduces the error compared to uniform approaches, and produces more consistent animated sequences

Hierarchical retargetting of 2D motion fields to the animation of 3D plant models

International audienceThe complexity of animating trees, shrubs and foliage is an impediment to the efficient and realistic depiction of natural environments. This paper presents an algorithm to extract, from a single video sequence, motion fields of real shrubs under the influence of wind, and to transfer this motion to the animation of complex, synthetic 3D plant models. The extracted motion is retargeted without requiring physical simulation. First, feature tracking is applied to the video footage, allowing the 2D position and velocity of automatically identified features to be clustered. A key contribution of the method is that the hierarchy obtained through statistical clustering can be used to synthesize a 2D hierarchical geometric structure of branches that terminates according to the cut-off threshold of a classification algorithm. This step extracts both the shape and the motion of a hierarchy of features groups that are identified as geometrical branches. The 2D hierarchy is then extended to three dimensions using the estimated spatial distribution of the features within each group. Another key contribution is that this 3D hierarchical structure can be efficiently used as a motion controller to animate any complex 3D model of similar but non-identical plants using a standard skinning algorithm. Thus, a single video source of a moving shrub becomes an input device for a large class of virtual shrubs. We illustrate the results on two examples of shrubs and one outdoor tree. Extensions to other outdoor plants are discussed

Mandoline: robust cut-cell generation for arbitrary triangle meshes

Although geometry arising "in the wild" most often comes in the form of a surface representation, a plethora of geometrical and physical applications require the construction of volumetric embeddings either of the geometry itself or the domain surrounding it. Cartesian cut-cell-based mesh generation provides an attractive solution in which volumetric elements are constructed from the intersection of the input surface geometry with a uniform or adaptive hexahedral grid. This choice, especially common in computational fluid dynamics, has the potential to efficiently generate accurate, surface-conforming cells; unfortunately, current solutions are often slow, fragile, or cannot handle many common topological situations. We therefore propose a novel, robust cut-cell construction technique for triangle surface meshes that explicitly computes the precise geometry of the intersection cells, even on meshes that are open or non-manifold. Its fundamental geometric primitive is the intersection of an arbitrary segment with an axis-aligned plane. Beginning from the set of intersection points between triangle mesh edges and grid planes, our bottom-up approach robustly determines cut-edges, cut-faces, and finally cut-cells, in a manner designed to guarantee topological correctness. We demonstrate its effectiveness and speed on a wide range of input meshes and grid resolutions, and make the code available as open source.This work is graciously supported by NSERC Discovery Grants (RGPIN-04360-2014 & RGPIN-2017-05524), NSERC Accelerator Grant (RGPAS-2017-507909), Connaught Fund (503114), and the Canada Research Chairs Program