A Visualization Tool Used to Develop New Photon Mapping Techniques

We present a visualisation tool aimed specifically at the development and optimisation of photon map denoising methods. Our tool allows the rapid testing of hypotheses and algorithms through the use of parallel coordinates, domain-specific scripting, color mapping and point plots. Interaction is carried out by brushing, adjusting parameters and focus-plus-context, and yields interactive visual feedback and debugging information. We demonstrate the use of the tool to explore high-dimensional photon map data, facilitating the discovery of novel parameter spaces which can be used to dissociate complex caustic illumination. We then show how these new parameterisations may be used to improve upon pre-existing noise removal methods in the context of the photon relaxation framework

Fast Blue-Noise Generation via Unsupervised Learning

—Blue noise is known for its uniformity in the spatial domain, avoiding the appearance of structures such as voids and clusters. Because of this characteristic, it has been adopted in a wide range of visual computing applications, such as image dithering, rendering and visualisation. This has motivated the development of a variety of generative methods for blue noise, with different trade-offs in terms of accuracy and computational performance. We propose a novel unsupervised learning approach that leverages a neural network architecture to generate blue noise masks with high accuracy and real-time performance, starting from a white noise input. We train our model by combining three unsupervised losses that work by conditioning the Fourier spectrum and intensity histogram of noise masks predicted by the network. We evaluate our method by leveraging the generated noise for two applications: grayscale blue noise masks for image dithering, and blue noise samples for Monte Carlo integration

Path manipulation strategies for rendering dynamic environments.

The current work introduces path manipulation as a tool that extends bidirectional path tracing to reuse paths in the temporal domain. Defined as an apparatus of sampling and reuse strategies, path manipulation reconstructs the subpaths that compose the light transport paths and addresses the restriction of static geometry commonly associated with Monte Carlo light transport simulations. By reconstructing and reusing subpaths, the path manipulation algorithm obviates the regeneration of the entire path collection, reduces the computational load of the original algorithm and supports scene dynamism. Bidirectional path tracing relies on local path sampling techniques to generate the paths of light in a synthetic environment. By using the information localized at path vertices, like the probability distribution, the sampling techniques construct paths progressively with distinct probability densities. Each probability density corresponds to a particular sampling technique, which accounts for specific illumination effects. Bidirectional path tracing uses multiple importance sampling to combine paths sampled with different techniques in low-variance estimators. The path sampling techniques and multiple importance sampling are the keys to the efficacy of bidirectional path tracing. However, the sampling techniques gained little attention beyond the generation and evaluation of paths. Bidirectional path tracing was designed for static scenes and thus it discards the generated paths immediately after the evaluation of their contributions. Limiting the lifespan of paths to a generation-evaluation cycle imposes a static use of paths and of sampling techniques. The path manipulation algorithm harnesses the potential of the sampling techniques to supplant the static manipulation of paths with a generation-evaluation-reuse cycle. An intra-subpath connectivity strategy was devised to reconnect the segregated chains of the subpaths invalidated by the scene alterations. Successful intra-subpath connections generate subpaths in multiple pieces by reusing subpath chains from prior frames. Subpaths are reconstructed generically, regardless of the subpath or scene dynamism type and without the need for predefined animation paths. The result is the extension of bidirectional path tracing to the temporal domain

Utilising path-vertex data to improve Monte Carlo global illumination.

Efficient techniques for photo-realistic rendering are in high demand across a wide array of industries. Notable applications include visual effects for film, entertainment and virtual reality. Less direct applications such as visualisation for architecture, lighting design and product development also rely on the synthesis of realistic and physically based illumination. Such applications assert ever increasing demands on light transport algorithms, requiring the computation of photo-realistic effects while handling complex geometry, light scattering models and illumination. Techniques based on Monte Carlo integration handle such scenarios elegantly and robustly, but despite seeing decades of focused research and wide commercial support, these methods and their derivatives still exhibit undesirable side effects that are yet to be resolved. In this thesis, Monte Carlo path tracing techniques are improved upon by utilizing path vertex data and intermediate radiance contributions readily available during rendering. This permits the development of novel progressive algorithms that render low noise global illumination while striving to maintain the desirable accuracy and convergence properties of unbiased methods. The thesis starts by presenting a discussion into optical phenomenon, physically based rendering and achieving photo realistic image synthesis. This is followed by in-depth discussion of the published theoretical and practical research in this field, with a focus on stochastic methods and modem rendering methodologies. This provides insight into the issues surrounding Monte Carlo integration both in the general and rendering specific contexts, along with an appreciation for the complexities of solving global light transport. Alternative methods that aim to address these issues are discussed, providing an insight into modem rendering paradigms and their characteristics. Thus, an understanding of the key aspects is obtained, that is necessary to build up and discuss the novel research and contributions to the field developed throughout this thesis. First, a path space filtering strategy is proposed that allows the path-based space of light transport to be classified into distinct subsets. This permits the novel combination of robust path tracing and recent progressive photon mapping algorithms to handle each subset based on the characteristics of the light transport in that space. This produces a hybrid progressive rendering technique that utilises the strengths of existing state of the art Monte Carlo and photon mapping methods to provide efficient and consistent rendering of complex scenes with vanishing bias. The second original contribution is a probabilistic image-based filtering and sample clustering framework that provides high quality previews of global illumination whilst remaining aware of high frequency detail and features in geometry, materials and the incident illumination. As will be seen, the challenges of edge-aware noise reduction are numerous and long standing, particularly when identifying high frequency features in noisy illumination signals. Discontinuities such as hard shadows and glossy reflections are commonly overlooked by progressive filtering techniques, however by dividing path space into multiple layers, once again based on utilising path vertex data, the overlapping illumination of varying intensities, colours and frequencies is more effectively handled. Thus noise is removed from each layer independent of features present in the remaining path space, effectively preserving such features

Progressive photon relaxation

We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping