Browsing by Author "Rainer, Gilles"
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Item Acquisition, Encoding and Rendering of Material Appearance Using Compact Neural Bidirectional Texture Functions(2021-11-23) Rainer, GillesThis thesis addresses the problem of photo-realistic rendering of real-world materials. Currently the most faithful approach to render an existing material is scanning the Bidirectional Reflectance Function (BTF), which relies on exhaustive acquisition of reflectance data from the material sample. This incurs heavy costs in terms of both capture times and memory requirements, meaning the main drawback is the lack of practicability. The scope of this thesis is two-fold: implementation of a full BTF pipeline (data acquisition, processing and rendering) and design of a compact neural material representation. We first present our custom BTF scanner, which uses a freely positionable camera and light source to acquire light- and view-dependent textures. During the processing phase, the textures are extracted from the images and rectified onto a unique grid using an estimated proxy surface. At rendering time, the rectification is reverted and the estimated height field additionally allows the preservation of material silhouettes. The main part of the thesis is the development of a neural BTF model that is both compact in memory and practical for rendering. Concretely, the material is modeled by a small fully-connected neural network, parametrized on light and view directions as well as a vector of latent parameters that describe the appearance of the point. We first show that one network can efficiently learn to reproduce the appearance of one given material. The second focus of our work is to find an efficient method to translate BTFs into our representation. Rather than training a new network instance for each new material, the latent space and network are shared, and we use an encoder network to quickly predict latent parameter networks for new, unseen materials. All contributions are geared towards making photo-realistic rendering with BTFs more common and practicable in computer graphics applications like games and virtual environments.Item Neural BRDF Representation and Importance Sampling(Ā© 2021 Eurographics ā The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Sztrajman, Alejandro; Rainer, Gilles; Ritschel, Tobias; Weyrich, Tim; Benes, Bedrich and Hauser, HelwigControlled capture of realāworld material appearance yields tabulated sets of highly realistic reflectance data. In practice, however, its high memory footprint requires compressing into a representation that can be used efficiently in rendering while remaining faithful to the original. Previous works in appearance encoding often prioritized one of these requirements at the expense of the other, by either applying highāfidelity array compression strategies not suited for efficient queries during rendering, or by fitting a compact analytic model that lacks expressiveness. We present a compact neural networkābased representation of BRDF data that combines highāaccuracy reconstruction with efficient practical rendering via builtāin interpolation of reflectance. We encode BRDFs as lightweight networks, and propose a training scheme with adaptive angular sampling, critical for the accurate reconstruction of specular highlights. Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known. We evaluate encoding results on isotropic and anisotropic BRDFs from multiple realāworld datasets, and importance sampling performance for isotropic BRDFs mapped to two different analytic models.Item Neural Precomputed Radiance Transfer(The Eurographics Association and John Wiley & Sons Ltd., 2022) Rainer, Gilles; Bousseau, Adrien; Ritschel, Tobias; Drettakis, George; Chaine, RaphaĆ«lle; Kim, Min H.Recent advances in neural rendering indicate immense promise for architectures that learn light transport, allowing efficient rendering of global illumination effects once such methods are trained. The training phase of these methods can be seen as a form of pre-computation, which has a long standing history in Computer Graphics. In particular, Pre-computed Radiance Transfer (PRT) achieves real-time rendering by freezing some variables of the scene (geometry, materials) and encoding the distribution of others, allowing interactive rendering at runtime. We adopt the same configuration as PRT - global illumination of static scenes under dynamic environment lighting - and investigate different neural network architectures, inspired by the design principles and theoretical analysis of PRT. We introduce four different architectures, and show that those based on knowledge of light transport models and PRT-inspired principles improve the quality of global illumination predictions at equal training time and network size, without the need for high-end ray-tracing hardware.Item Neural Shading Fields for Efficient Facial Inverse Rendering(The Eurographics Association and John Wiley & Sons Ltd., 2023) Rainer, Gilles; Bridgeman, Lewis; Ghosh, Abhijeet; Chaine, RaphaĆ«lle; Deng, Zhigang; Kim, Min H.Given a set of unstructured photographs of a subject under unknown lighting, 3D geometry reconstruction is relatively easy, but reflectance estimation remains a challenge. This is because it requires disentangling lighting from reflectance in the ambiguous observations. Solutions exist leveraging statistical, data-driven priors to output plausible reflectance maps even in the underconstrained single-view, unknown lighting setting. We propose a very low-cost inverse optimization method that does not rely on data-driven priors, to obtain high-quality diffuse and specular, albedo and normal maps in the setting of multi-view unknown lighting. We introduce compact neural networks that learn the shading of a given scene by efficiently finding correlations in the appearance across the face. We jointly optimize the implicit global illumination of the scene in the networks with explicit diffuse and specular reflectance maps that can subsequently be used for physically-based rendering. We analyze the veracity of results on ground truth data, and demonstrate that our reflectance maps maintain more detail and greater personal identity than state-of-the-art deep learning and differentiable rendering methods.Item Unified Neural Encoding of BTFs(The Eurographics Association and John Wiley & Sons Ltd., 2020) Rainer, Gilles; Ghosh, Abhijeet; Jakob, Wenzel; Weyrich, Tim; Panozzo, Daniele and Assarsson, UlfRealistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the desire for compact and continuously parametrized models akin to analytic BRDFs; however, fitting BRDF parameters to complex data such as BTF texels can prove challenging, as models tend to describe restricted function spaces that cannot encompass real-world behavior. Recent advances in this area have increasingly relied on neural representations that are trained to reproduce acquired reflectance data. The associated training process is extremely costly and must typically be repeated for each material. Inspired by autoencoders, we propose a unified network architecture that is trained on a variety of materials, and which projects reflectance measurements to a shared latent parameter space. Similarly to SVBRDF fitting, real-world materials are represented by parameter maps, and the decoder network is analog to the analytic BRDF expression (also parametrized on light and view directions for practical rendering application). With this approach, encoding and decoding materials becomes a simple matter of evaluating the network. We train and validate on BTF datasets of the University of Bonn, but there are no prerequisites on either the number of angular reflectance samples, or the sample positions. Additionally, we show that the latent space is well-behaved and can be sampled from, for applications such as mipmapping and texture synthesis.