Incremental Labelling of Voronoi Vertices for Shape Reconstruction

We present an incremental Voronoi vertex labelling algorithm for approximating contours, medial axes and dominant points (high curvature points) from 2D point sets. Though there exist many number of algorithms for reconstructing curves, medial axes or dominant points, a unified framework capable of approximating all the three in one place from points is missing in the literature. Our algorithm estimates the normals at each sample point through poles (farthest Voronoi vertices of a sample point) and uses the estimated normals and the corresponding tangents to determine the spatial locations (inner or outer) of the Voronoi vertices with respect to the original curve. The vertex classification helps to construct a piece‐wise linear approximation to the object boundary. We provide a theoretical analysis of the algorithm for points non‐uniformly (ε‐sampling) sampled from simple, closed, concave and smooth curves. The proposed framework has been thoroughly evaluated for its usefulness using various test data. Results indicate that even sparsely and non‐uniformly sampled curves with outliers or collection of curves are faithfully reconstructed by the proposed algorithm.     

Decoupled Space and Time Sampling of Motion and Defocus Blur for Unified Rendering of Transparent and Opaque Objects

We propose a unified rendering approach that jointly handles motion and defocus blur for transparent and opaque objects at interactive frame rates. Our key idea is to create a sampled representation of all parts of the scene geometry that are potentially visible at any point in time for the duration of a frame in an initial rasterization step. We store the resulting temporally‐varying fragments (t‐fragments) in a bounding volume hierarchy which is rebuild every frame using a fast spatial median construction algorithm. This makes our approach suitable for interactive applications with dynamic scenes and animations. Next, we perform spatial sampling to determine all t‐fragments that intersect with a specific viewing ray at any point in time. Viewing rays are sampled according to the lens uv‐sampling for depth‐of‐field effects. In a final temporal sampling step, we evaluate the predetermined viewing ray/t‐fragment intersections for one or multiple points in time. This allows us to incorporate all standard shading effects including transparency. We describe the overall framework, present our GPU implementation, and evaluate our rendering approach with respect to scalability, quality, and performance.     

Flow‐Based Temporal Selection for Interactive Volume Visualization

We present an approach to adaptively select time steps from time‐dependent volume data sets for an integrated and comprehensive visualization. This reduced set of time steps not only saves cost, but also allows to show both the spatial structure and temporal development in one combined rendering. Our selection optimizes the coverage of the complete data on the basis of a minimum‐cost flow‐based technique to determine meaningful distances between time steps. As both optimal solutions of the involved transport and selection problem are prohibitively expensive, we present new approaches that are significantly faster with only minor deviations. We further propose an adaptive scheme for the progressive incorporation of new time steps. An interactive volume raycaster produces an integrated rendering of the selected time steps, and their computed differences are visualized in a dedicated chart to provide additional temporal similarity information. We illustrate and discuss the utility of our approach by means of different data sets from measurements and simulation.     

Fast Ray Sorting and Breadth‐First Packet Traversal for GPU Ray Tracing

We present a novel approach to ray tracing execution on commodity graphics hardware using CUDA. We decompose a standard ray tracing algorithm into several data‐parallel stages that are mapped efficiently to the massively parallel architecture of modern GPUs. These stages include: ray sorting into coherent packets, creation of frustums for packets, breadth‐first frustum traversal through a bounding volume hierarchy for the scene, and localized ray‐primitive intersections. We utilize the well known parallel primitives scan and segmented scan in order to process irregular data structures, to remove the need for a stack, and to minimize branch divergence in all stages. Our ray sorting stage is based on applying hash values to individual rays, ray stream compression, sorting and decompression. Our breadth‐first BVH traversal is based on parallel frustum‐bounding box intersection tests and parallel scan per each BVH level. We demonstrate our algorithm with area light sources to get a soft shadow effect and show that our concept is reasonable for GPU implementation. For the same data sets and ray‐primitive intersection routines our pipeline is ～3x faster than an optimized standard depth first ray tracing implemented in one kernel.     

Real‐time Depth of Field Rendering via Dynamic Light Field Generation and Filtering

We present a new algorithm for efficient rendering of high‐quality depth‐of‐field (DoF) effects. We start with a single rasterized view (reference view) of the scene, and sample the light field by warping the reference view to nearby views. We implement the algorithm using NVIDIA's CUDA to achieve parallel processing, and exploit the atomic operations to resolve visibility when multiple pixels warp to the same image location. We then directly synthesize DoF effects from the sampled light field. To reduce aliasing artifacts, we propose an image‐space filtering technique that compensates for spatial undersampling using MIP mapping. The main advantages of our algorithm are its simplicity and generality. We demonstrate interactive rendering of DoF effects in several complex scenes. Compared to existing methods, ours does not require ray tracing and hence scales well with scene complexity.     

Recent Advances in Projection Mapping Algorithms,Hardware and Applications

This State‐of‐the‐Art‐Report covers the recent advances in research fields related to projection mapping applications. We summarize the novel enhancements to simplify the 3D geometric calibration task, which can now be reliably carried out either interactively or automatically using self‐calibration methods. Furthermore, improvements regarding radiometric calibration and compensation as well as the neutralization of global illumination effects are summarized. We then introduce computational display approaches to overcome technical limitations of current projection hardware in terms of dynamic range, refresh rate, spatial resolution, depth‐of‐field, view dependency, and color space. These technologies contribute towards creating new application domains related to projection‐based spatial augmentations. We summarize these emerging applications, and discuss new directions for industries.     

Adaptive Temporal Sampling for Volumetric Path Tracing of Medical Data

Monte‐Carlo path tracing techniques can generate stunning visualizations of medical volumetric data. In a clinical context, such renderings turned out to be valuable for communication, education, and diagnosis. Because a large number of computationally expensive lighting samples is required to converge to a smooth result, progressive rendering is the only option for interactive settings: Low‐sampled, noisy images are shown while the user explores the data, and as soon as the camera is at rest the view is progressively refined. During interaction, the visual quality is low, which strongly impedes the user's experience. Even worse, when a data set is explored in virtual reality, the camera is never at rest, leading to constantly low image quality and strong flickering. In this work we present an approach to bring volumetric Monte‐Carlo path tracing to the interactive domain by reusing samples over time. To this end, we transfer the idea of temporal antialiasing from surface rendering to volume rendering. We show how to reproject volumetric ray samples even though they cannot be pinned to a particular 3D position, present an improved weighting scheme that makes longer history trails possible, and define an error accumulation method that downweights less appropriate older samples. Furthermore, we exploit reprojection information to adaptively determine the number of newly generated path tracing samples for each individual pixel. Our approach is designed for static, medical data with both volumetric and surface‐like structures. It achieves good‐quality volumetric Monte‐Carlo renderings with only little noise, and is also usable in a VR context.     

Topic Tomographies (TopTom): a visual approach to distill information from media streams

In this paper we present Top Tom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user‐generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low‐dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. Top Tom implements a batch processing pipeline able to run both in near‐real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast‐like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.     

Object‐order template‐based approach for stereoscopic volume rendering

Stereoscopic volume rendering provides powerful depth information, but it takes a long time to render two‐eye images. Previous algorithms based on reprojection methods project the result of one view of a stereo pair into the other instead of rendering a new one completely. Because of inaccurate mapping between the two images, the quality of the reprojected image is not satisfactory. This paper presents a new algorithm to preserve the accuracy of both images with very little increase in computation time. The efficiency of the new algorithm comes from the use of ray templates and object‐order processing. This algorithm makes two different templates for each eye and renders two images simultaneously, tracing the volume only once in object order. We also extend the algorithm to support image‐space supersampling by using more ray templates. Experimental results show that the image quality of the new algorithm is not only comparable with that of ray casting but also the rendering speed is near that of the interactive shear–warp algorithm employing object‐order processing and spatial data coherency. Copyright © 1999 John Wiley & Sons, Ltd.     

Active Scene Understanding via Online Semantic Reconstruction

We propose a novel approach to robot‐operated active understanding of unknown indoor scenes, based on online RGBD reconstruction with semantic segmentation. In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene. Our algorithm is built on top of a volumetric depth fusion framework and performs real‐time voxel‐based semantic labeling over the online reconstructed volume. The robot is guided by an online estimated discrete viewing score field (VSF) parameterized over the 3D space of 2D location and azimuth rotation. VSF stores for each grid the score of the corresponding view, which measures how much it reduces the uncertainty (entropy) of both geometric reconstruction and semantic labeling. Based on VSF, we select the next best views (NBV) as the target for each time step. We then jointly optimize the traverse path and camera trajectory between two adjacent NBVs, through maximizing the integral viewing score (information gain) along path and trajectory. Through extensive evaluation, we show that our method achieves efficient and accurate online scene parsing during exploratory scanning.     

Exploiting Repetitions for Image‐Based Rendering of Facades

Street‐level imagery is now abundant but does not have sufficient capture density to be usable for Image‐Based Rendering (IBR) of facades. We present a method that exploits repetitive elements in facades ‐ such as windows ‐ to perform data augmentation, in turn improving camera calibration, reconstructed geometry and overall rendering quality for IBR. The main intuition behind our approach is that a few views of several instances of an element provide similar information to many views of a single instance of that element. We first select similar instances of an element from 3–4 views of a facade and transform them into a common coordinate system, creating a “platonic” element. We use this common space to refine the camera calibration of each view of each instance and to reconstruct a 3D mesh of the element with multi‐view stereo, that we regularize to obtain a piecewise‐planar mesh aligned with dominant image contours. Observing the same element under multiple views also allows us to identify reflective areas ‐ such as glass panels ‐ which we use at rendering time to generate plausible reflections using an environment map. Our detailed 3D mesh, augmented set of views, and reflection mask enable image‐based rendering of much higher quality than results obtained using the input images directly.     

An Exploratory Technique for Coherent Visualization of Time‐varying Volume Data

The selection of an appropriate global transfer function is essential for visualizing time‐varying simulation data. This is especially challenging when the global data range is not known in advance, as is often the case in remote and in‐situ visualization settings. Since the data range may vary dramatically as the simulation progresses, volume rendering using local transfer functions may not be coherent for all time steps. We present an exploratory technique that enables coherent classification of time‐varying volume data. Unlike previous approaches, which require pre‐processing of all time steps, our approach lets the user explore the transfer function space without accessing the original 3D data. This is useful for interactive visualization, and absolutely essential for in‐situ visualization, where the entire simulation data range is not known in advance. Our approach generates a compact representation of each time step at rendering time in the form of ray attenuation functions, which are used for subsequent operations on the opacity and color mappings. The presented approach offers interactive exploration of time‐varying simulation data that alleviates the cost associated with reloading and caching large data sets.     

Reinterpretable Imager: Towards Variable Post‐Capture Space,Angle and Time Resolution in Photography

We describe a novel multiplexing approach to achieve tradeoffs in space, angle and time resolution in photography. We explore the problem of mapping useful subsets of time‐varying 4D lightfields in a single snapshot. Our design is based on using a dynamic mask in the aperture and a static mask close to the sensor. The key idea is to exploit scene‐specific redundancy along spatial, angular and temporal dimensions and to provide a programmable or variable resolution tradeoff among these dimensions. This allows a user to reinterpret the single captured photo as either a high spatial resolution image, a refocusable image stack or a video for different parts of the scene in post‐processing. A lightfield camera or a video camera forces a‐priori choice in space‐angle‐time resolution. We demonstrate a single prototype which provides flexible post‐capture abilities not possible using either a single‐shot lightfield camera or a multi‐frame video camera. We show several novel results including digital refocusing on objects moving in depth and capturing multiple facial expressions in a single photo.     

GPU‐Based Spherical Light Field Rendering with Per‐Fragment Depth Correction

Image‐based rendering techniques are a powerful alternative to traditional polygon‐based computer graphics. This paper presents a novel light field rendering technique which performs per‐pixel depth correction of rays for high‐quality reconstruction. Our technique stores combined RGB and depth values in a parabolic 2D texture for every light field sample acquired at discrete positions on a uniform spherical setup. Image synthesis is implemented on the GPU as a fragment program which extracts the correct image information from adjacent cameras for each fragment by applying per‐pixel depth correction of rays. We show that the presented image‐based rendering technique provides a significant improvement compared to previous approaches. We explain two different rendering implementations which make use of a uniform parametrisation to minimise disparity problems and ensure full six degrees of freedom for virtual view synthesis. While one rendering algorithm implements an iterative refinement approach for rendering light fields with per pixel depth correction, the other approach employs a raycaster, which provides superior rendering quality at moderate frame rates. GPU based per‐fragment depth correction of rays, used in both implementations, helps reducing ghosting artifacts to a non‐noticeable amount and provides a rendering technique that performs without exhaustive pre‐processing for 3D object reconstruction and without real‐time ray‐object intersection calculations at rendering time.     

Ray Classification for Accelerated BVH Traversal

For ray tracing based methods, traversing a hierarchical acceleration data structure takes up a substantial portion of the total rendering time. We propose an additional data structure which cuts off large parts of the hierarchical traversal. We use the idea of ray classification combined with the hierarchical scene representation provided by a bounding volume hierarchy. We precompute short arrays of indices to subtrees inside the hierarchy and use them to initiate the traversal for a given ray class. This arrangement is compact enough to be cache‐friendly, preventing the method from negating its traversal gains by excessive memory traffic. The method is easy to use with existing renderers which we demonstrate by integrating it to the PBRT renderer. The proposed technique reduces the number of traversal steps by 42% on average, saving around 15% of time of finding ray‐scene intersection on average.     

Deep Video‐Based Performance Synthesis from Sparse Multi‐View Capture

We present a deep learning based technique that enables novel‐view videos of human performances to be synthesized from sparse multi‐view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non‐rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel‐view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel‐view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel‐view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine‐scale details of the first step results. This opens up the possibility of high‐quality low‐cost video‐based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state‐of‐the‐art approaches when only sparse views are available.     

A Temporally Adaptive Material Point Method with Regional Time Stepping

Spatially and temporally adaptive algorithms can substantially improve the computational efficiency of many numerical schemes in computational mechanics and physics‐based animation. Recently, a crucial need for temporal adaptivity in the Material Point Method (MPM) is emerging due to the potentially substantial variation of material stiffness and velocities in multi‐material scenes. In this work, we propose a novel temporally adaptive symplectic Euler scheme for MPM with regional time stepping (RTS), where different time steps are used in different regions. We design a time stepping scheduler operating at the granularity of small blocks to maintain a natural consistency with the hybrid particle/grid nature of MPM. Our method utilizes the Sparse Paged Grid (SPGrid) data structure and simultaneously offers high efficiency and notable ease of implementation with a practical multi‐threaded particle‐grid transfer strategy. We demonstrate the efficacy of our asynchronous MPM method on various examples including elastic objects, granular media, and fluids.     

Ray Tracing Generalized Tube Primitives: Method and Applications

We present a general high‐performance technique for ray tracing generalized tube primitives. Our technique efficiently supports tube primitives with fixed and varying radii, general acyclic graph structures with bifurcations, and correct transparency with interior surface removal. Such tube primitives are widely used in scientific visualization to represent diffusion tensor imaging tractographies, neuron morphologies, and scalar or vector fields of 3D flow. We implement our approach within the OSPRay ray tracing framework, and evaluate it on a range of interactive visualization use cases of fixed‐ and varying‐radius streamlines, pathlines, complex neuron morphologies, and brain tractographies. Our proposed approach provides interactive, high‐quality rendering, with low memory overhead.     

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.     

Memory‐Efficient Interactive Online Reconstruction From Depth Image Streams

We describe how the pipeline for 3D online reconstruction using commodity depth and image scanning hardware can be made scalable for large spatial extents and high scanning resolutions. Our modified pipeline requires less than 10% of the memory that is required by previous approaches at similar speed and resolution. To achieve this, we avoid storing a 3D distance field and weight map during online scene reconstruction. Instead, surface samples are binned into a high‐resolution binary voxel grid. This grid is used in combination with caching and deferred processing of depth images to reconstruct the scene geometry. For pose estimation, GPU ray‐casting is performed on the binary voxel grid. A one‐to‐one comparison to level‐set ray‐casting in a distance volume indicates slightly lower pose accuracy. To enable unlimited spatial extents and store acquired samples at the appropriate level of detail, we combine a hash map with a hierarchical tree representation.