ARTISIS : ARTIficial intelligence for image syntheSIS
Image synthesis (also called rendering) consists in computing an image from a virtual 3D scene. It is a widespread technique used in many fields such as cinema, advertising, industrial design and so on. Rendering is a light transport simulation problem, defined by a Fredholm integral equation called the rendering equation [Kaj86]. The rendering equation is difficult to solve so many numerical methods have been proposed. The Path-Tracing algorithm [Kaj86] is a straightforward Monte-Carlo method which successfully solves the rendering equation. Many sampling techniques have been proposed to improve Path-Tracing, however the algorithm can still be very inefficient for scenes with complex illumination conditions. The Metropolis Light Transport algorithm [VG97] is an adaptation of the Metropolis algorithm [Met+53] to light transport. It is a Monte-Carlo Markov Chain method which handles complex scenes more efficiently than Path-Tracing. It is classically used in modern renderers. In the artificial intelligence field, the Metropolis algorithm, also known as Simulated Annealing [AK89; Hwa88], is a popular and robust algorithm for optimization problems. Alternatively, there exists a certain number of state of the art algorithms used in optimization that are not necessarily known by the rendering community. For instance Evolutionary Algorithms, and in particular for discrete problems, Genetic Algorithms [Hol92; Mit98], are also known for their robustness and their efficiency. In this project, the main idea is to investigate how artificial intelligence algorithms other than the Simulated Annealing can be used for rendering images.
Aim and novelty
The main objective of the project is to experiment evolutionary optimization algorithms applied to the rendering problem. We believe this can be a win-win collaboration between the two fields. In the rendering field, it would bring alternative methods to compute the rendering equation. In the evolutionary optimization field, it would bring new applications of existing algorithms and allow possibilities of creating new techniques (for example, new mutation and crossover operators specially designed for the rendering equation). More precisely, in this project, we are interested in two kinds of artificial intelligence algorithms. First, we would consider optimization algorithms (in particular, Genetic Algorithms) to compute the rendering equation. Rendering is an integration problem generally solved numerically by random sampling (Monte- Carlo integration). More advanced algorithms, such as Metropolis Light Transport, try to generate a sampling distribution which fits the integration problem, i.e. an optimization problem. In [LM03], Laskey and Myers compared several stochastic search algorithms classically used in artificial intelligence and a Metropolis-inspired algorithm. In this project, we would follow the opposite approach: investigate how AI algorithms can solve a problem usually solved with Metropolis. We have already implemented and compared such AI and Metropolis algorithms for solving a simplified “Metropolis problem” [CE05] and obtained encouraging results. In a second part of the project, we would consider using decision making algorithms classically used in the artificial intelligence field, for the rendering problem. Indeed, rendering algorithms are generally implemented using various decision strategies and heuristics, which are also studied in the artificial intelligence field. For example, an image is generally rendered using a fixed number of samples per pixel, however lighting conditions can greatly differ between image areas. Consequently, it may be more efficient to allocate the samples non-uniformly (i.e. compute more samples in complex areas), which can be done by using AI decision algorithms such as multi-armed bandit [ACBF02]. We have already tested this idea for an optimization problem and we think it would be applicable to the rendering problem too.
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[Mit98] Melanie Mitchell. An introduction to genetic algorithms. MIT press, 1998.
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