Fischer, Michael; (2025) Efficient and Accurate Optimization in Inverse Rendering and Computer Graphics. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Efficient and accurate representation of graphic assets, a long-standing task in the graphics community, has achieved new heights with the advent of learning-based methods by representing visual appearance as neural networks. Surprisingly, such visual appearance networks are often trained from scratch – an expensive operation that ignores potentially helpful information from previous training runs. This thesis therefore introduces Metappearance, an algorithm which optimizes over optimization itself and enables orders of magnitude faster training times at indistinguishable visual quality while retaining the network’s adaptability to new, unseen data. Moreover, even a fully converged network, albeit a smooth function, does not guarantee optimization success when employed in an inverse rendering scenario. In fact, it is common for inverse rendering to exhibit plateaus – regions of zero gradient – in the cost function, which hinder gradient-based optimization from converging. Chapter 4 therefore introduces an algorithm that smooths out such plateaus by convolving the rendering equation with a Gaussian blur kernel and thus successfully optimizes scenarios where other, rigid methods fail to converge. Finally, while recent research has shown that specialized treatment of the Efficient and accurate representation of graphic assets, a long-standing task in the graphics community, has achieved new heights with the advent of learning-based methods by representing visual appearance as neural networks. Surprisingly, such visual appearance networks are often trained from scratch – an expensive operation that ignores potentially helpful information from previous training runs. This thesis therefore introduces Metappearance, an algorithm which optimizes over optimization itself and enables orders of magnitude faster training times at indistinguishable visual quality while retaining the network’s adaptability to new, unseen data. Moreover, even a fully converged network, albeit a smooth function, does not guarantee optimization success when employed in an inverse rendering scenario. In fact, it is common for inverse rendering to exhibit plateaus – regions of zero gradient – in the cost function, which hinder gradient-based optimization from converging. Chapter 4 therefore introduces an algorithm that smooths out such plateaus by convolving the rendering equation with a Gaussian blur kernel and thus successfully optimizes scenarios where other, rigid methods fail to converge. Finally, while recent research has shown that specialized treatment of the renderer’s internals can yield correct, usable gradients, there is no unified, systematic way of differentiating through arbitrary, black-box graphics pipelines. We therefore introduce the concept of neural surrogates, which allow differentiating through arbitrary forward models without requiring access to, or making any assumptions on, the rendering pipeline’s internals. We show that our neural surrogate losses can successfully optimize various graphics tasks and scale well to high dimensions, a domain where traditional derivative-free optimizers often do not converge.

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