BTF - Paper Copilot

Neural Biplane Representation for BTF Rendering and Acquisition

Jiahui Fan, Beibei Wang, Milos Hasan, Jian Yang, Ling-Qi Yan

Nanjing University of Science and Technology; Nankai University; Adobe Research; University of California, Santa Barbara

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Bidirectional Texture Functions (BTFs) are able to represent complex materials with greater generality than traditional analytical models. This holds true for both measured real materials and synthetic ones. Recent advancements in neural BTF representations have significantly reduced storage costs, making them more practical for use in rendering. These representations typically combine spatial feature (latent) textures with neural decoders that handle angular dimensions per spatial location. However, these models have yet to combine fast compression and inference, accuracy, and generality. In this paper, we propose a biplane representation for BTFs, which uses a feature texture in the half-vector domain as well as the spatial domain. This allows the learned representation to encode high-frequency details in both the spatial and angular domains. Our decoder is small yet general, meaning it is trained once and fixed. Additionally, we optionally combine this representation with a neural offset module for parallax and masking effects. Our model can represent a broad range of BTFs and has fast compression and inference due to its lightweight architecture. Furthermore, it enables a simple way to capture BTF data. By taking about 20 cell phone photos with a collocated camera and flash, our model can plausibly recover the entire BTF, despite never observing function values with differing view and light directions. We demonstrate the effectiveness of our model in the acquisition of many measured materials, including challenging materials such as fabrics.

Related Works

Neural based BTF/BRDF compression; Exhaustive BTF capture; Lightweight neural SVBRDF acquisition

NeuMIP: Multi-Resolution Neural Materials

Alexandr Kuznetsov, Krishna Mullia, Zexiang Xu, Milo

University of California, San Diego; Adobe Research

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We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which allows rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.

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Prefiltering and Mipmapping; Bidirectional Texture Functions; Neural Reflectance; Other Neural Material Methods

Rendering Neural Materials on Curved Surfaces

Alexandr Kuznetsov, Xuezheng Wang, Krishna Mullia, Fujun Luan, Zexiang Xu, Milo

University of California, San Diego; Adobe Research

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Neural material reflectance representations address some limitations of traditional analytic BRDFs with parameter textures; they can theoretically represent any material data, whether a complex synthetic microgeometry with displacements, shadows and interreflections, or real measured reflectance. However, they still approximate the material on an infinite plane, which prevents them from correctly handling silhouette and parallax effects for viewing directions close to grazing. The goal of this paper is to design a neural material representation capable of correctly handling such silhouette effects. We extend the neural network query to take surface curvature information as input, while the query output is extended to return a transparency value in addition to reflectance. We train the new neural representation on synthetic data that contains queries spanning a variety of surface curvatures. We show an ability to accurately represent complex silhouette behavior that would traditionally require more expensive and less flexible techniques, such as on-the-fly geometry displacement or ray marching.

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Displacement Mapping and Its Variations; BTFs; Neural Reflectance; Thin Volumetric Shells

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NeuMIP

Reflectance and Texture of Real-World Surfaces

Kristin J. Dana, Shree K. Nayar, Bram Van Ginneken, Jan J. Koenderink

Columbia University; Utrecht University

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In this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on the geometry of imaging conditions. We discuss a new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction. We present a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. We describe the methods involved in collecting the database as well as the importqance and uniqueness of this database for computer graphics. A related quantity to the BTF is the familiar BRDF (bidirectional reflectance distribution function). The measurement methods involved in the BTF database are conducive to simultaneous measurement of the BRDF. Accordingly, we also present a BRDF database with reflectance measurements for over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. Both of these unique databases are publicly available and have important implications for computer graphics.

Material Classification Based on Training Data Synthesized Using a BTF Database

Michael Weinmann, Juergen Gall, and Reinhard Klein

Institute of Computer Science, University of Bonn

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To cope with the richness in appearance variation found in real-world data under natural illumination, we propose to synthesize training data capturing these variations for material classification. Using synthetic training data created from separately acquired material and illumination characteristics allows to overcome the problems of existing material databases which only include a tiny fraction of the possible real-world conditions under controlled laboratory environments. However, it is essential to utilize a representation for material appearance which preserves fine details in the reflectance behavior of the digitized materials. As BRDFs are not sufficient for many materials due to the lack of modeling mesoscopic effects, we present a high-quality BTF database with 22,801 densely measured view-light configurations including surface geometry measurements for each of the 84 measured material samples. This representation is used to generate a database of synthesized images depicting the materials under different view-light conditions with their characteristic surface geometry using image-based lighting to simulate the complexity of real-world scenarios. We demonstrate that our synthesized data allows classifying materials under complex real-world scenarios.

Related Works

Databases; Synthetic Training Data

Unified Neural Encoding of BTFs

Gilles Rainer, Abhijeet Ghosh, Wenzel Jakob, Tim Weyrich

University College London; Imperial College London; Ecole Polytechnique Fédérale de Lausanne (EPFL)

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Abstract

Realistic 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.

Related Works

Fitting parametric models; Latent spaces of appearance; Neural encoding of appearance

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Rainer et al, full BTF

Category: BTF

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