Using Pantora’s anisotropic textile preset, the overall acquisition is performed by rotating the material at five 45◦ steps, while capturing images with all four cameras. For each captured image, individual LED point lights (color filtered or not) are switched on. The entire process results in 348 (4 cameras × 3 rotation steps × 29 LEDs) point lit panchromatic images. 100 of those point lit images have additional color information through band filtered illumination available. Finally, the device captures images for each turntable rotation, viewed by each camera, while moving the linear light source. Depending on a glossiness preset, the linear light source declination angle step size is 4◦ (#steps = 14) for low, 2◦ (#steps = 28) for medium and 0.5◦ (#steps = 165) for high gloss materials. This results in (#steps×5×4) ∈ {280,560,3300} additional images for low, medium or high gloss materials respectively.

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/ .

In this paper X-Rite presents a file format for exchanging digital material appearance. The format is a vital part of X-Rite’s appearance initiative consisting of a new generation of appearance capturing devices. In order to make the usage of measured appearance as simple as possible for users, a broad support by software vendors is essential. Therefore, XRite aims to initiate the assembly of a consortium of hardand software vendors, members of the scientific community and users, which would be responsible for the further development and standardization of the format.

A recurrent problem in the analysis of microscopy images is to quantify the number and size of spots on a homogeneous background. Unfortunately, segmenting the individual spots becomes unreliable when they are close together, or when the image contains noise and artifacts. On the other hand, manual counting and line-scan measurements are prone to bias and too time-consuming to be used in high-throughput microscopy. In this work, we derive novel per-pixel measures of spot scale and density from Total Variation Flow, a partial differential equation that changes the intensities of image regions at a rate inverse to their scale. On simulated, phantom, and real-world data from Stimulated Emission Depletion (STED) microscopy, we demonstrate the robustness of our novel method relative to a standard segmentation-based approach.

Despite the widely recognized importance of symmetric second order tensor fields in medicine and engineering, the visualization of data uncertainty in tensor fields is still in its infancy. A recently proposed tensorial normal distribution, involving a fourth order covariance tensor, provides a mathematical description of how different aspects of the tensor field, such as trace, anisotropy, or orientation, vary and covary at each point. However, this wealth of information is far too rich for a human analyst to take in at a single glance, and no suitable visualization tools are available. We propose a novel approach that facilitates visual analysis of tensor covariance at multiple levels of detail. We start with a visual abstraction that uses slice views and direct volume rendering to indicate large-scale changes in the covariance structure, and locations with high overall variance. We then provide tools for interactive exploration, making it possible to drill down into different types of variability, such as in shape or orientation. Finally, we allow the analyst to focus on specific locations of the field, and provide tensor glyph animations and overlays that intuitively depict confidence intervals at those points. Our system is demonstrated by investigating the effects of measurement noise on diffusion tensor MRI, and by analyzing two ensembles of stress tensor fields from solid mechanics.

The combination of long established techniques in morphometrics with novel shape modeling approaches in geometry processing has opened new ways of visualizations of shape variability in different application areas like biology, medicine, epidemiology and agriculture. For the first time highly resolved 3D representations became accessible for statistical analysis as well as visualizations. In order to reveal causes for shape variability targeted statistical analysis correlating shape features against external and internal factors is necessary but due to the complexity of the problem often not feasible in an automated way. Therefore, visual analytics methods found their way into the field of morphometrics. This led to numerous publications in recent years that might be subsumed under the novel term visual shape analytics. In this paper we try to put these works into the context of visual analytics, outline the basic principles underlying these approaches and review the current state of the art. Finally, future challenges and possibilities in visual shape analytics are identified.

Large image deformations pose a challenging problem for the visualization and statistical analysis of 3D image ensembles which have a multitude of applications in biology and medicine. Simple linear interpolation in the tangent space of the ensemble introduces artifactual anatomical structures that hamper the application of targeted visual shape analysis techniques. In this work we make use of the theory of stationary velocity fields to facilitate interactive non-linear image interpolation and plausible extrapolation for high quality rendering of large deformations and devise an efficient image warping method on the GPU. This does not only improve quality of existing visualization techniques, but opens up a field of novel interactive methods for shape ensemble analysis. Taking advantage of the efficient non-linear 3D image warping, we showcase four visualizations: 1) browsing on-the-fly computed group mean shapes to learn about shape differences between specific classes, 2) interactive reformation to investigate complex morphologies in a single view, 3) likelihood volumes to gain a concise overview of variability and 4) streamline visualization to show variation in detail, specifically uncovering its component tangential to a reference surface. Evaluation on a real world dataset shows that the presented method outperforms the state-of-the-art in terms of visual quality while retaining interactive frame rates. A case study with a domain expert was performed in which the novel analysis and visualization methods are applied on standard model structures, namely skull and mandible of different rodents, to investigate and compare influence of phylogeny, diet and geography on shape. The visualizations enable for instance to distinguish (population-)normal and pathological morphology, assist in uncovering correlation to extrinsic factors and potentially support assessment of model quality.

Gaining insight into anatomic co variation helps the understanding of organismic shape variability in general and is of particular interest for delimiting morphological modules. Generation of hypotheses on structural co variation is undoubtedly a highly creative process, and as such, requires an exploratory approach. In this work we propose a new local anatomic covariance tensor which enables interactive visualizations to explore co variation at different levels of detail, stimulating rapid formation and (qualitative) evaluation of hypotheses. The effectiveness of the presented approach is demonstrated on a μCT dataset of mouse mandibles for which results from the literature are successfully reproduced, while providing a more detailed representation of co variation compared to state-of-the-art methods.

A recurrent problem in biological image analysis is to quantify the number and size of spots on a homogeneous background. Most automated approaches rely on segmenting the individual spots, which becomes unreliable when the image contains artifacts, noise, or confounding objects. Therefore, practitioners often resort to tedious and time-consuming manual counting and measurements. As an alternative, we propose a visual analytics approach to this problem. It is based on Total Variation Flow, a partial differential equation that changes the intensities of image regions at a rate inverse to their scale. From this, we derive novel quantitative per-pixel measures of scale and density, and we show how the results can be combined with tools for visualization and selection to achieve a fast summary of median size and spot density in an image. Given a set of images, our framework places them on a 2D map that makes it easy to quickly compare them with respect to spot sizes and density. Our system is applied to real-world data from Stimulated Emission Depletion (STED) microscopy.

The exploration of medical imaging datasets often requires a segmentation of the images according to different materials or structures. Model-based algorithms excel in finding closed boundary contours enclosing the structure to be segmented. However, porose structures like Spongiosa have a complex topology and do not exhibit a unique single closed boundary contour. In order to enable segmentation of such complex structures we suggest a new algorithmic framework based on a Reeb graph representing the topological information. Each node in the graph corresponds to a connected region of voxels in a specific image slice while edges indicate connected regions between adjacent slices. Starting with a coarse segmentation, the corresponding graph is refined at critical nodes and the resulting connected components of the graph provide the final segmentation. We present two strategies for identifying critical nodes, one solely based on dynamic thresholding and one based on a single user specified pre-segmentation. The approach is evaluated on a dataset of 193 mCT scans of rodent skulls which are segmented into skull, left and right mandible.

Creating geometrically detailed mesh animations is an involved and resource-intensive process in digital content creation. In this work we present a method to rapidly combine available sparse motion capture data with existing mesh sequences to produce a large variety of new animations. The key idea is to model shape changes correlated to the pose of the animated object via a part-based statistical shape model. We observe that compact linear models suffice for a segmentation into nearly rigid parts. The same segmentation further guides the parameterization of the pose which is learned in conjunction with the marker movement. Besides the inherent high geometric detail, further benefits of the presented method arise from its robustness against errors in segmentation and pose parameterization. Due to efficiency of both learning and synthesis phase, our model allows to interactively steer virtual avatars based on few markers extracted from video data or input devices like the Kinect sensor.

A common technique in 3D shape analysis is to describe shape variability using a statistical deformation model (SDM). In contrast to the use of sparse landmark data for volume data this SDM is based on dense registrations of the input shapes. For a valuable exploration of the shape space in the setting of biological morphometrics we identified two prominent objectives for visual investigation. The first objective is to detect possible shape variations between anatomically different groups of individuals. The second is to integrate and exploit expert knowledge about relevant regions on the shapes. To meet the first objective, we advocate the use of dimensionality reduction methods combined with a parameterization defined on user specified classifications. This idea was already successfully applied in data-driven reflectance models and also turns out to be valuable in the context of biological morphometry, as it allows for intuitive exploration of shape variations. The second objective can be achieved by an appropriate weighted linear analysis which delivers a better approximation of shape variations in local neighbourhoods of a user defined region of interest. The methods were applied to real-world biological datasets of rodent mandibles and validated in cooperation with the MPI for Evolutionary Biology. For this purpose, we provide an interactive dynamic visualization of the shape space based on a custom GPU raycaster. A special feature of our implementation is that it builds the SDM directly on dense registrations of the volumes and does thereby not rely on a specific non-rigid registration method.

In high energy physics the structure of matter is investigated through particle accelerator experiments where particle collisions (events) occur at such high energies that new particles are produced. Providing tools for interactive visual inspection of billions of such events occurring in an experiment in an intuitive way is a challenging task. In order to solve this problem we built on previous approaches for visual browsing through image databases and extend them in several ways in order to allow efficient navigation through the collision event datasets. The key features of our novel browsing technique are its applicability to the very large event datasets, a more intuitive selection method for specifying a region of interest, and finally a clustering-based technique that further simplifies and improves the navigation process. We demonstrate the potential of our novel visual inspection system by integrating it into an event display application for the COMPASS experiment at CERN.