Light Transport Simulation with Non-Monte Carlo Approaches

如今，数字媒体行业的内容创作在很大程度上依赖于逼真图像的创作。示例应用程序包括电影、工业设计和科学可视化。这种逼真的图像是通过模拟光在现实世界中的传播而产生的，通常称为光传输模拟。除了数字媒体，光传输模拟也正在成为分析灯光观测数据（传感器测量）的主要工具，并通过生成逼真的虚拟街景来训练自动驾驶汽车。光传输模拟计算到达传感器（或眼睛）的光的强度，给定光源的输入数据和物体的外观和形状。由于光可以多次从物体上反射回来，因此从光源出发的光到达传感器的可能路径是无限多的。光传输模拟通常通过随机选择若干路径来近似这样的无限路径集，然后使用这些随机选择的路径估计到达传感器的光强度。这种方法通常被称为蒙特卡罗方法。虽然在数字媒体行业中广泛使用，但已知使用这种蒙特卡罗方法进行光传输模拟有几个基本问题。最常见的问题是，它产生的噪声图像由于其数值误差。由于照明通常是平滑的，噪声是一个不受欢迎的工件，应该去除。这也与蒙特卡罗方法对光的可能路径的假设很少有关，而光的可能路径确实有许多限制。例如，光的能量和传感器处的强度都是非负的，但蒙特卡罗并没有明确地使用这个事实。蒙特卡罗方法也忽略了光输运的递归性质。例如，三次反弹后的照明应该与四次反弹后的照明相似，但蒙特卡罗方法独立地模拟不同数量的反弹。本研究计划的目的是挑战普遍认为蒙特卡罗是光传输模拟唯一可行的方法。我们的目标是通过开发新的方法来解决蒙特卡罗在轻传输模拟中应用的基本问题，如果我们只依赖传统的蒙特卡罗，这是不可能的。我和我的学生们通过探索各种不适合传统蒙特卡洛范式的方法，并行地研究不同的问题。本研究项目开辟了一种基于非蒙特卡罗方法的光输运模拟新范式。这些结果有助于对光输运模拟的基本理解，并有助于更有效地模拟光输运。这种高效的光传输模拟对于适应数字媒体行业对视觉细节日益增长的需求是必要的。

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.