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Abstract In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage arbitrary representations of plasma quantities and easily incorporate them into a non-linear optimisation framework. The efficiency of our method enables not only estimation of a physically plausible image of plasma, but also recovery of the neutral Deuterium distribution from imaging and midplane measurements alone. We demonstrate our method with three different levels of complexity showing first that a poloidal neutrals density distribution can be recovered from imaging alone, second that the distributions of neutral Deuterium, electron density and electron temperature can be recovered jointly, and finally, that this can be done in the presence of realistic imaging systems that incorporate sensor cropping and quantisation. 

Abstract We present a deep learning based solution for separating the direct and global light transport components from a single photograph captured under high frequency structured lighting with a co‐axial projector‐camera setup. We employ an architecture with one encoder and two decoders that shares information between the encoder and the decoders, as well as between both decoders to ensure a consistent decomposition between both light transport components. Furthermore, our deep learning separation approach does not require binary structured illumination, allowing us to utilize the full resolution capabilities of the projector. Consequently, our deep separation network is able to achieve high fidelity decompositions for lighting frequency sensitive features such as subsurface scattering and specular reflections. We evaluate and demonstrate our direct and global separation method on a wide variety of synthetic and captured scenes. 

Abstract We propose a novel image‐driven fitting strategy for isotropic BRDFs. Whereas existing BRDF fitting methods minimize a cost function directly on the error between the fitted analytical BRDF and the measured isotropic BRDF samples, we also take into account the resulting material appearance in visualizations of the BRDF. This change of fitting paradigm improves the appearance reproduction fidelity, especially for analytical BRDF models that lack the expressiveness to reproduce the measured surface reflectance. We formulate BRDF fitting as a two‐stage process that first generates a series of candidate BRDF fits based only on the BRDF error with measured BRDF samples. Next, from these candidates, we select the BRDF fit that minimizes the visual error. We demonstrate qualitatively and quantitatively improved fits for the Cook‐Torrance and GGX microfacet BRDF models. Furthermore, we present an analysis of the BRDF fitting results, and show that the image‐driven isotropic BRDF fits generalize well to other light conditions, and that depending on the measured material, a different weighting of errors with respect to the measured BRDF is necessary. 

We present a novel and flexible learning-based method for generating tileable image sets. Our method goes beyond simple self-tiling, supporting sets of mutually tileable images that exhibit a high degree of diversity. To promote diversity we decouple structure from content by foregoing explicit copying of patches from an exemplar image. Instead we leverage the prior knowledge of natural images and textures embedded in large-scale pretrained diffusion models to guide tile generation constrained by exterior boundary conditions and a text prompt to specify the content. By carefully designing and selecting the exterior boundary conditions, we can reformulate the tile generation process as an inpainting problem, allowing us to directly employ existing diffusion-based inpainting models without the need to retrain a model on a custom training set. We demonstrate the flexibility and efficacy of our content-aware tile generation method on different tiling schemes, such as Wang tiles, from only a text prompt. Furthermore, we introduce a novel Dual Wang tiling scheme that provides greater texture continuity and diversity than existing Wang tile variants.