Analytic and Neural Approaches for Complex Light Transport

Abstract

The goal of rendering is to produce a photorealistic image of the given 3D scene description. Physically based rendering simulates the physics of the light as it travels and interacts with objects in the scene before finally reaching the camera sensor. Monte Carlo methods have been the go-to approach for physically based rendering. They are general and robust but introduce noise and are computationally expensive. Recent advancements in hardware, algorithms, and denoising techniques have enabled realtime applications of Monte Carlo methods. However, complex scenes still demand high sample counts. In this thesis, we explore and present the utilization of analytic and neural approaches for physically based rendering. Analytic methods offer noise-free renderings but are less general and may introduce bias. In recent years, neural-based approaches have gained traction, offering a balance between generality and computational efficiency. We compare and contrast the traditional Monte Carlo-based methods and emerging analytic and neural network-based methods. We then propose analytic and neural solutions to two challenging cases: direct lighting with many area lights and efficient rendering of glinty appearances on specular normal-mapped surfaces. Direct lighting from many area light sources is challenging due to variance from both choosing an important light and then a point on it. Existing methods weigh the contribution of all lights by estimating their effect on the shading point. We propose to extend one such method by using analytic methods to improve the estimation of the light’s contribution. This enhancement accelerates the convergence of the algorithm, making it more efficient for scenes with many dynamic lights. The second case deals with the challenge of rendering glinty appearances on normal mapped specular surfaces efficiently. Traditional Monte Carlo methods struggle with this task due to the rapidly changing spatial characteristics of microstructures. Our solution introduces a novel method supporting spatially varying roughness based on a neural histogram, offering both memory and compute efficiency. Additionally, full direct illumination integration is computed analytically for all light directions with minimal computational effort, resulting in improved quality compared to previous approaches. Through comprehensive analysis and experimentation, this thesis contributes to the advancement of rendering techniques, shedding light on the trade-offs between different methods and providing insights into their practical applications for achieving photorealistic rendering