Light Transport Simulation with Non-Monte Carlo Approaches

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Content creation in the digital media industry today relies heavily on the creation of photorealistic images. Example applications include movies, industry design, and scientific visualization. Such photorealistic images are generated by simulating the propagation of light in the real world, which is commonly called light transport simulation. In addition to the digital media, light transport simulation is also becoming a primary tool for analyzing the observed data (sensor measurements) of lights and training autonomous vehicles by generating photorealistic virtual street views. Light transport simulation computes the intensity of light arriving at the sensor (or the eye) given the input data of light sources and appearances and shapes of objects. Since light can bounce off objects any number of times, there are infinitely many possible paths of how light starting from a light source can arrive at the sensor. Light transport simulation commonly approximates such an infinite set of paths by randomly selecting several paths, and then estimates the intensity of light arriving at the sensor using those randomly selected paths. This approach is commonly called a Monte Carlo method. While widely used in the digital media industry, light transport simulation using this Monte Carlo approach is known to have several fundamental issues. The most commonly known issue is that it produces noisy images due to its numerical error. Since illumination is usually smooth, noise is an undesirable artifact that should be removed. It is also related to the fact that the Monte Carlo methods assume very little about the possible paths of light that indeed have many constraints. For example, both the energy of light and the intensity at the sensor are non-negative, but Monte Carlo does not explicitly use this fact. Monte Carlo approaches also ignore the recursive nature of light transport. For instance, illumination after three bounces should be similar to illumination after four bounces, but Monte Carlo approaches independently simulate different numbers of bounces. The objective of this research program is to challenge the common belief that Monte Carlo is the only viable approach for light transport simulation. Our goal is to address the fundamental issues of the application of Monte Carlo to light transport simulation by developing novel approaches that are impossible if we rely only on conventional Monte Carlo. My students and I work on different issues in parallel by exploring various approaches that do not fit within the paradigm of conventional Monte Carlo. This research program opens up a new paradigm of light transport simulation based on non-Monte Carlo approaches. The results contribute to the fundamental understandings of light transport simulation and lead to more efficient simulation of light transport. Such efficient light transport simulation is necessary to accommodate the ever-growing demands for visual details in the digital media industry.