Image-based appearance measurements are fundamentally limited in spatial resolution by the acquisition hardware. Due to the ever-increasing resolution of displaying hardware, high-resolution representations of digital material appearance are desireable for authentic renderings. In the present paper, we demonstrate that high-resolution bidirectional texture functions (BTFs) for materials can be obtained from low-resolution measurements using single-image convolutional neural network (CNN) architectures for image super-resolution. In particular, we show that this approach works for high-dynamic-range data and produces consistent BTFs, even though it operates on an image-by-image basis. Moreover, the CNN can be trained on down-sampled measured data, therefore no high-resolution ground-truth data, which would be difficult to obtain, is necessary. We train and test our method's performance on a large-scale BTF database and evaluate against the current state-of-the-art in BTF super-resolution, finding superior performance.

Real-time reflectance reconstruction under uncontrolled illumination conditions is well-known to be a challenging task due to the complex interplay of scene geometry, surface reflectance and illumination. Nonetheless, recent works succeed in recovering both unknown reflectance and illumination in an uncontrolled setting. However, they are either limited regarding the scene complexity (single objects / homogeneous materials) or are not suitable for real-time applications. Our proposed method enables the recovery of heterogeneous surface reflectance (multiple objects and spatially varying materials) in complex scenes at real-time frame rates. We achieve this goal in the following way: First, we perform a 3D scene reconstruction from an input RGB-D stream in real-time. We then use a deep learning based method to estimate Ward BRDF parameters from observations gathered from individual segmented scene objects. Subsequently we refine these reflectance parameters to allow for spatial variations across the object surfaces. We evaluate our method on synthetic scenes and successfully apply it to real-world data.

The analysis and modeling of cloth has received a lot of attention in recent years. While recent approaches are focused on woven cloth, we present a novel practical approach for the inference of more complex knitwear structures as well as the respective knitting instructions from only a single image without attached annotations. Knitwear is produced by repeating instances of the same pattern, consisting of grid-like arrangements of a small set of basic stitch types. Our framework addresses the identification and localization of the occurring stitch types, which is challenging due to huge appearance variations. The resulting coarsely localized stitch types are used to infer the underlying grid structure as well as for the extraction of the knitting instruction of pattern repeats, taking into account principles of Gestalt theory. Finally, the derived instructions allow the reproduction of the knitting structures, either as renderings or by actual knitting, as demonstrated in several examples.

Abstract The use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results of equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large‐scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re‐renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni‐bonn.de/svbrdfs/ .

We present a compact and efficient representation of spectra for accurate rendering using more than three dimensions. While tristimulus color spaces are sufficient for color display, a spectral renderer has to simulate light transport per wavelength. Consequently, emission spectra and surface albedos need to be known at each wavelength. It is practical to store dense samples for emission spectra but for albedo textures, the memory requirements of this approach are unreasonable. Prior works that approximate dense spectra from tristimulus data introduce strong errors under illuminants with sharp peaks and in indirect illumination. We represent spectra by an arbitrary number of Fourier coefficients. However, we do not use a common truncated Fourier series because its ringing could lead to albedos below zero or above one. Instead, we present a novel approach for reconstruction of bounded densities based on the theory of moments. The core of our technique is our bounded maximum entropy spectral estimate. It uses an efficient closed form to compute a smooth signal between zero and one that matches the given Fourier coefficients exactly. Still, a ground truth that localizes all of its mass around a few wavelengths can be reconstructed adequately. Therefore, our representation covers the full gamut of valid reflectances. The resulting textures are compact because each coefficient can be stored in 10 bits. For compatibility with existing tristimulus assets, we implement a mapping from tristimulus color spaces to three Fourier coefficients. Using three coefficients, our technique gives state of the art results without some of the drawbacks of related work. With four to eight coefficients, our representation is superior to all existing representations. Our focus is on offline rendering but we also demonstrate that the technique is fast enough for real-time rendering.