CHC+RT: Coherent Hierarchical Culling for Ray Tracing

We propose a new technique for in‐core and out‐of‐core GPU ray tracing using a generalization of hierarchical occlusion culling in the style of the CHC++ method. Our method exploits the rasterization pipeline and hardware occlusion queries in order to create coherent batches of work for localized shader‐based ray tracing kernels. By combining hierarchies in both ray space and object space, the method is able to share intermediate traversal results among multiple rays. We exploit temporal coherence among similar ray sets between frames and also within the given frame. A suitable management of the current visibility state makes it possible to benefit from occlusion culling for less coherent ray types like diffuse reflections. Since large scenes are still a challenge for modern GPU ray tracers, our method is most useful for scenes with medium to high complexity, especially since our method inherently supports ray tracing highly complex scenes that do not fit in GPU memory. For in‐core scenes our method is comparable to CUDA ray tracing and performs up to 5.94 × better than pure shader‐based ray tracing.     

A Survey of Perceptually Motivated 3D Visualization of Medical Image Data

This survey provides an overview of perceptually motivated techniques for the visualization of medical image data, including physics‐based lighting techniques as well as illustrative rendering that incorporate spatial depth and shape cues. Additionally, we discuss evaluations that were conducted in order to study the perceptual effects of these visualization techniques as compared to conventional techniques. These evaluations assessed depth and shape perception with depth judgment, orientation matching, and related tasks. This overview of existing techniques and their evaluation serves as a basis for defining the evaluation process of medical visualizations and to discuss a research agenda.     

Temporally Coherent and Artistically Intended Stylization of Feature Lines Extracted from 3D Models

In this paper, we propose a method to maintain the temporal coherence of stylized feature lines extracted from 3D models and preserve an artistically intended stylization provided by the user. We formally define the problem of combining spatio‐temporal continuity and artistic intention as a weighted energy minimization problem of competing constraints. The proposed method updates the style properties to provide real‐time smooth transitions from current to goal stylization, by assuring first‐ and second‐order temporal continuity, as well as spatial continuity along each stroke. The proposed weighting scheme guarantees that the stylization of strokes maintains motion coherence with respect to the apparent motion of the underlying surface in consecutive frames. This weighting scheme emphasizes temporal continuity for small apparent motions where the human vision system is able to keep track of the scene, and prioritizes the artistic intention for large apparent motions where temporal coherence is not expected. The proposed method produces temporally coherent and visually pleasing animations without the flickering artifacts of previous methods, while also maintaining the artistic intention of a goal stylization provided by the user.     

Sliceplorer: 1D slices for multi‐dimensional continuous functions

Multi‐dimensional continuous functions are commonly visualized with 2D slices or topological views. Here, we explore 1D slices as an alternative approach to show such functions. Our goal with 1D slices is to combine the benefits of topological views, that is, screen space efficiency, with those of slices, that is a close resemblance of the underlying function. We compare 1D slices to 2D slices and topological views, first, by looking at their performance with respect to common function analysis tasks. We also demonstrate 3 usage scenarios: the 2D sinc function, neural network regression, and optimization traces. Based on this evaluation, we characterize the advantages and drawbacks of each of these approaches, and show how interaction can be used to overcome some of the shortcomings.     

Inertial Steady 2D Vector Field Topology

Vector field topology is a powerful and matured tool for the study of the asymptotic behavior of tracer particles in steady flows. Yet, it does not capture the behavior of finite‐sized particles, because they develop inertia and do not move tangential to the flow. In this paper, we use the fact that the trajectories of inertial particles can be described as tangent curves of a higher dimensional vector field. Using this, we conduct a full classification of the first‐order critical points of this higher dimensional flow, and devise a method to their efficient extraction. Further, we interactively visualize the asymptotic behavior of finite‐sized particles by a glyph visualization that encodes the outcome of any initial condition of the governing ODE, i.e., for a varying initial position and/or initial velocity. With this, we present a first approach to extend traditional vector field topology to the inertial case.     

Intrinsic Decompositions for Image Editing

Intrinsic images are a mid‐level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground‐truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image‐editing applications.     

Error analysis of estimators that use combinations of stochastic sampling strategies for direct illumination

We present a theoretical analysis of error of combinations of Monte Carlo estimators used in image synthesis. Importance sampling and multiple importance sampling are popular variance‐reduction strategies. Unfortunately, neither strategy improves the rate of convergence of Monte Carlo integration. Jittered sampling (a type of stratified sampling), on the other hand is known to improve the convergence rate. Most rendering software optimistically combine importance sampling with jittered sampling, hoping to achieve both. We derive the exact error of the combination of multiple importance sampling with jittered sampling. In addition, we demonstrate a further benefit of introducing negative correlations (antithetic sampling) between estimates to the convergence rate. As with importance sampling, antithetic sampling is known to reduce error for certain classes of integrands without affecting the convergence rate. In this paper, our analysis and experiments reveal that importance and antithetic sampling, if used judiciously and in conjunction with jittered sampling, may improve convergence rates. We show the impact of such combinations of strategies on the convergence rate of estimators for direct illumination.     

Decoupled Opacity Optimization for Points,Lines and Surfaces

Displaying geometry inflow visualization is often accompanied by occlusion problems, making it difficult to perceive information that is relevant in the respective application. In a recent technique, named opacity optimization, the balance of occlusion avoidance and the selection of meaningful geometry was recognized to be a view‐dependent, global optimization problem. The method solves a bounded‐variable least‐squares problem, which minimizes energy terms for the reduction of occlusion, background clutter, adding smoothness and regularization. The original technique operates on an object‐space discretization and was shown for line and surface geometry. Recently, it has been extended to volumes, where it was solved locally per ray by dropping the smoothness energy term and replacing it by pre‐filtering the importance measure. In this paper, we pick up the idea of splitting the opacity optimization problem into two smaller problems. The first problem is a minimization with analytic solution, and the second problem is a smoothing of the obtained minimizer in object‐space. Thereby, the minimization problem can be solved locally per pixel, making it possible to combine all geometry types (points, lines and surfaces) consistently in a single optimization framework. We call this decoupled opacity optimization and apply it to a number of steady 3D vector fields.     

Finite‐Time Mass Separation for Comparative Visualizations of Inertial Particles

The visual analysis of flows with inertial particle trajectories is a challenging problem because time‐dependent particle trajectories additionally depend on mass, which gives rise to an infinite number of possible trajectories passing through every point in space‐time. This paper presents an approach to a comparative visualization of the inertial particles’ separation behavior. For this, we define the Finite‐Time Mass Separation (FTMS), a scalar field that measures at each point in the domain how quickly inertial particles separate that were released from the same location but with slightly different mass. Extracting and visualizing the mass that induces the largest separation provides a simplified view on the critical masses. By using complementary coordinated views, we additionally visualize corresponding inertial particle trajectories in space‐time by integral curves and surfaces. For a quantitative analysis, we plot Euclidean and arc length‐based distances to a reference particle over time, which allows to observe the temporal evolution of separation events. We demonstrate our approach on a number of analytic and one real‐world unsteady 2D field.     

Similarity Voting based Viewpoint Selection for Volumes

Previous viewpoint selection methods in volume visualization are generally based on some deterministic measures of viewpoint quality. However, they may not express the familiarity and aesthetic sense of users for features of interest. In this paper, we propose an image‐based viewpoint selection model to learn how visualization experts choose representative viewpoints for volumes with similar features. For a given volume, we first collect images with similar features, and these images reflect the viewpoint preferences of the experts when visualizing these features. Each collected image tallies votes to the viewpoints with the best matching based on an image similarity measure, which evaluates the spatial shape and appearance similarity between the collected image and the rendered image from the viewpoint. The optimal viewpoint is the one with the most votes from the collected images, that is, the viewpoint chosen by most visualization experts for similar features. We performed experiments on various volumes available in volume visualization, and made comparisons with traditional viewpoint selection methods. The results demonstrate that our model can select more canonical viewpoints, which are consistent with human perception.     

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High‐Dimensional Data

Linear projections are one of the most common approaches to visualize high‐dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.     

FlexyFont: Learning Transferring Rules for Flexible Typeface Synthesis

Maintaining consistent styles across glyphs is an arduous task in typeface design. In this work we introduce FlexyFont, a flexible tool for synthesizing a complete typeface that has a consistent style with a given small set of glyphs. Motivated by a key fact that typeface designers often maintain a library of glyph parts to achieve a consistent typeface, we intend to learn part consistency between glyphs of different characters across typefaces. We take a part assembling approach by firstly decomposing the given glyphs into semantic parts and then assembling them according to learned sets of transferring rules to reconstruct the missing glyphs. To maintain style consistency, we represent the style of a font as a vector of pairwise part similarities. By learning a distribution over these feature vectors, we are able to predict the style of a novel typeface given only a few examples. We utilize a popular machine learning method as well as retrieval‐based methods to quantitatively assess the performance of our feature vector, resulting in favorable results. We also present an intuitive interface that allows users to interactively create novel typefaces with ease. The synthesized fonts can be directly used in real‐world design.     

Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization

In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.     

Selective Degree Elevation for Multi‐Sided Bézier Patches

This paper presents a method to selectively elevate the degree of an S‐Patch of arbitrary dimension. We consider not only S‐Patches with 2D domains but 3D and higher‐dimensional domains as well, of which volumetric cage deformations are a subset. We show how to selectively insert control points of a higher degree patch into a lower degree patch while maintaining the polynomial reproduction order of the original patch. This process allows the user to elevate the degree of only one portion of the patch to add new degrees of freedom or maintain continuity with adjacent patches without elevating the degree of the entire patch, which could create far more degrees of freedom than necessary. Finally we show an application to cage‐based deformations where we increase the number of control points by elevating the degree of a subset of cage faces. The result is a cage deformation with higher degree triangular Bézier functions on a subset of cage faces but no interior control points.     

High‐resolution 360 Video Foveated Stitching for Real‐time VR

In virtual reality (VR) applications, the contents are usually generated by creating a 360° Video panorama of a real‐world scene. Although many capture devices are being released, getting high‐resolution panoramas and displaying a virtual world in real‐time remains challenging due to its computationally demanding nature. In this paper, we propose a real‐time 360° Video foveated stitching framework, that renders the entire scene in different level of detail, aiming to create a high‐resolution panoramic Video in real‐time that can be streamed directly to the client. Our foveated stitching algorithm takes Videos from multiple cameras as input, combined with measurements of human visual attention (i.e. the acuity map and the saliency map), can greatly reduce the number of pixels to be processed. We further parallelize the algorithm using GPU to achieve a responsive interface and validate our results via a user study. Our system accelerates graphics computation by a factor of 6 on a Google Cardboard display.     

Generalized Diffusion Curves: An Improved Vector Representation for Smooth‐Shaded Images

This paper generalizes the well‐known Diffusion Curves Images (DCI), which are composed of a set of Bezier curves with colors specified on either side. These colors are diffused as Laplace functions over the image domain, which results in smooth color gradients interrupted by the Bezier curves. Our new formulation allows for more color control away from the boundary, providing a similar expressive power as recent Bilaplace image models without introducing associated issues and computational costs. The new model is based on a special Laplace function blending and a new edge blur formulation. We demonstrate that given some user‐defined boundary curves over an input raster image, fitting colors and edge blur from the image to the new model and subsequent editing and animation is equally convenient as with DCIs. Numerous examples and comparisons to DCIs are presented.     

Reduced Aggregate Scattering Operators for Path Tracing

Aggregate scattering operators (ASOs) describe the overall scattering behavior of an asset (i.e., an object or volume, or collection thereof) accounting for all orders of its internal scattering. We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing. Our approach is modular avoiding costly and inflexible scene‐dependent precomputation. This is achieved by decoupling light transport within and outside of each asset, and precomputing on a per‐asset level. We store the internal transport in a reduced‐dimensional subspace tailored to the structure of the asset geometry, its scattering behavior, and typical illumination conditions, allowing the ASOs to maintain good accuracy with modest memory requirements. The precomputed ASO can be reused across all instances of the asset and across multiple scenes. We augment ASOs with functionality enabling multi‐bounce importance sampling, fast short‐circuiting of complex light paths, and compact caching, while retaining rapid progressive preview rendering. We demonstrate the benefits of our ASOs by efficiently path tracing scenes containing many instances of objects with complex inter‐reflections or multiple scattering.     

Linear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Data

One main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster‐based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster‐aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real‐life benchmark high‐dimensional data sets.     

Minimum‐Displacement Overlap Removal for Geo‐referenced Data Visualization

Given a set of rectangles embedded in the plane, we consider the problem of adjusting the layout to remove all overlap while preserving the orthogonal order of the rectangles. The objective is to minimize the displacement of the rectangles. We call this problem Minimum -Displacement Overlap Removal (mdor ). Our interest in this problem is motivated by the application of displaying metadata of archaeological sites. Because most existing overlap removal algorithms are not designed to minimize displacement while preserving orthogonal order, we present and compare several approaches which are tailored to our particular usecase. We introduce a new overlap removal heuristic which we call re Arrange . Although conceptually simple, it is very effective in removing the overlap while keeping the displacement small. Furthermore, we propose an additional procedure to repair the orthogonal order after every iteration, with which we extend both our new heuristic and PRISM, a widely used overlap removal algorithm. We compare the performance of both approaches with and without this order repair method. The experimental results indicate that re Arrange is very effective for heterogeneous input data where the overlap is concentrated in few dense regions.     

Adaptive multi‐scale analysis for point‐based surface editing

This paper presents a tool that enables the direct editing of surface features in large point‐clouds or meshes. This is made possible by a novel multi‐scale analysis of unstructured point‐clouds that automatically extracts the number of relevant features together with their respective scale all over the surface. Then, combining this ingredient with an adequate multi‐scale decomposition allows us to directly enhance or reduce each feature in an independent manner. Our feature extraction is based on the analysis of the scale‐variations of locally fitted surface primitives combined with unsupervised learning techniques. Our tool may be applied either globally or locally, and millions of points are handled in real‐time. The resulting system enables users to accurately edit complex geometries with minimal interaction.