Among recent graph drawing (GD) methods, tsNET creates high quality layouts but suffers from a very high runtime due to its underlying reliance on the t-SNE projection technique. We address this problem by presenting NNP-NET, a method that adapts NNP, a projection technique that can project high-dimensional datasets linearly in the data size, to handle both unweighted and weighted graphs, with layout quality being very close to the ground-truth tsNET. We also exploit NNP's built-in out-of-sample ability to enable NNP-NET to project time-dependent (dynamic) graphs while striking a good balance between layout stability and good layout quality. We show experiments that outline how NNP-NET can handle very large graphs - up to 50 million nodes and 108 million edges faster than all other comparable methods we are aware of while also yielding good quality metric values.

tsNET is a recent graph drawing (GD) method that creates high quality layouts but suffers from a very high runtime. We present a new GD method, NNP-NET, which reduces tsNET’s time complexity to generate layouts for very large graphs in seconds. Additionally, we extend tsNET to support drawing graphs with edge weights. We accomplish this by replacing tsNET’s t-SNE projection with Neural Network Projection (NNP), a fast dimensionality reduction (DR) method that can imitate any given DR method. Our experiments show that NNP-NET gets good quality results when compared to other state-of-the art GD methods while yielding a better computational scalability.

Abstract A smart contract (SC) is a programme stored in the Ethereum blockchain by a contract‐creation transaction. SC developers deploy an instance of the SC and attempt to execute it in exchange for a fee, paid in Ethereum coins (Ether). If the computation needed for their execution turns out to be larger than the effort proposed by the developer (i.e., the gasLimit), their client instantiation will not be completed successfully. In this paper, we examine SCs from 11 Ethereum blockchain‐oriented software projects hosted on GitHub.com , and we evaluate the resources needed for their deployment (i.e., the gasUsed). For each of these contracts, we also extract a suite of object‐oriented metrics, to evaluate their structural characteristics. Our results show a statistically significant correlation between some of the object‐oriented (OO) metrics and the resources consumed on the Ethereum blockchain network when deploying SCs. This result has a direct impact on how Ethereum developers engage with a SC: evaluating its structural characteristics, they will be able to produce a better estimate of the resources needed to deploy it. Other results show specific source code metrics to be prioritised based on application domains when the projects are clustered based on common themes.

Abstract We introduce a bidirectional reflectance distribution function (BRDF) model for the rendering of materials that exhibit hazy reflections, whereby the specular reflections appear to be flanked by a surrounding halo. The focus of this work is on artistic control and ease of implementation for real‐time and off‐line rendering. We propose relying on a composite material based on a pair of arbitrary BRDF models; however, instead of controlling their physical parameters, we expose perceptual parameters inspired by visual experiments [ VBF17 ]. Our main contribution then consists in a mapping from perceptual to physical parameters that ensures the resulting composite BRDF is valid in terms of reciprocity, positivity and energy conservation. The immediate benefit of our approach is to provide direct artistic control over both the intensity and extent of the haze effect, which is not only necessary for editing purposes, but also essential to vary haziness spatially over an object surface. Our solution is also simple to implement as it requires no new importance sampling strategy and relies on existing BRDF models. Such a simplicity is key to approximating the method for the editing of hazy gloss in real‐time and for compositing.

We are developing a novel device for measuring hand power grip using frustrated total internal reflection of light in acrylic. Our method uses a force sensitive resistor to calibrate the force of a power grip as a function of the area and light intensity. This research is work in progress but results so far augur well for its applicability in medical and other application areas. The grip measurement device allows the patient and doctor to see the change in grip over time and projects this information directly onto the back of the patient's hand.

The visual system constantly adapts to different luminance levels when viewing natural scenes. The state of visual adaptation is the key parameter in many visual models. While the time-course of such adaptation is well understood, there is little known about the spatial pooling that drives the adaptation signal. In this work we propose a new empirical model of local adaptation, that predicts how the adaptation signal is integrated in the retina. The model is based on psychophysical measurements on a high dynamic range (HDR) display. We employ a novel approach to model discovery, in which the experimental stimuli are optimized to find the most predictive model. The model can be used to predict the steady state of adaptation, but also conservative estimates of the visibility (detection) thresholds in complex images. We demonstrate the utility of the model in several applications, such as perceptual error bounds for physically based rendering, determining the backlight resolution for HDR displays, measuring the maximum visible dynamic range in natural scenes, simulation of afterimages, and gaze-dependent tone mapping.

Many people who first see a high dynamic range (HDR) display get the impression that it is a 3D display, even though it does not produce any binocular depth cues. Possible explanations of this effect include contrast-based depth induction and the increased realism due to the high brightness and contrast that makes an HDR display "like looking through a window". In this paper we test both of these hypotheses by comparing the HDR depth illusion to real binocular depth cues using a carefully calibrated HDR stereoscope. We confirm that contrast-based depth induction exists, but it is a vanishingly weak depth cue compared to binocular depth cues. We also demonstrate that for some observers, the increased contrast of HDR displays indeed increases the realism. However, it is highly observer-dependent whether reduced, physically correct, or exaggerated contrast is perceived as most realistic, even in the presence of the real-world reference scene. Similarly, observers differ in whether reduced, physically correct, or exaggerated stereo 3D is perceived as more realistic. To accommodate the binocular depth perception and realism concept of most observers, display technologies must offer both HDR contrast and stereo personalization.

Several approaches attempt to reproduce the appearance of a scotopic low-light night scene on a photopic display (“day-for-night”) by introducing color desaturation, loss of acuity, and the Purkinje shift toward blue colors. We argue that faithful stereo reproduction of night scenes on photopic stereo displays requires manipulation of not only color but also binocular disparity. To this end, we performed a psychophysics experiment to devise a model of disparity at scotopic luminance levels. Using this model, we can match binocular disparity of a scotopic stereo content displayed on a photopic monitor to the disparity that would be perceived if the scene was actually scotopic. The model allows for real-time computation of common stereo content as found in interactive applications such as simulators or computer games.

Image-based rendering (IBR) creates realistic images by enriching simple geometries with photographs, e.g., mapping the photograph of a building façade onto a plane. However, as soon as the viewer moves away from the correct viewpoint, the image in the retina becomes distorted, sometimes leading to gross misperceptions of the original geometry. Two hypotheses from vision science state how viewers perceive such image distortions, one claiming that they can compensate for them (and therefore perceive scene geometry reasonably correctly), and one claiming that they cannot compensate (and therefore can perceive rather significant distortions). We modified the latter hypothesis so that it extends to street-level IBR. We then conducted a rigorous experiment that measured the magnitude of perceptual distortions that occur with IBR for façade viewing. We also conducted a rating experiment that assessed the acceptability of the distortions. The results of the two experiments were consistent with one another. They showed that viewers' percepts are indeed distorted, but not as severely as predicted by the modified vision science hypothesis. From our experimental results, we develop a predictive model of distortion for street-level IBR, which we use to provide guidelines for acceptability of virtual views and for capture camera density. We perform a confirmatory study to validate our predictions, and illustrate their use with an application that guides users in IBR navigation to stay in regions where virtual views yield acceptable perceptual distortions.

Immersive spaces such as 4-sided displays with stereo viewing and high-quality tracking provide a very engaging and realistic virtual experience. However, walking is inherently limited by the restricted physical space, both due to the screens (limited translation) and the missing back screen (limited rotation). In this paper, we propose three novel locomotion techniques that have three concurrent goals: keep the user safe from reaching the translational and rotational boundaries; increase the amount of real walking and finally, provide a more enjoyable and ecological interaction paradigm compared to traditional controller-based approaches. We notably introduce the "Virtual Companion", which uses a small bird to guide the user through VEs larger than the physical space. We evaluate the three new techniques through a user study with travel-to-target and path following tasks. The study provides insight into the relative strengths of each new technique for the three aforementioned goals. Specifically, if speed and accuracy are paramount, traditional controller interfaces augmented with our novel warning techniques may be more appropriate; if physical walking is more important, two of our paradigms (extended Magic Barrier Tape and Constrained Wand) should be preferred; last, fun and ecological criteria would favor the Virtual Companion.

Abstract Image‐based rendering (IBR) techniques allow users to create interactive 3D visualizations of scenes by taking a few snapshots. However, despite substantial progress in the field, the main barrier to better quality and more efficient IBR visualizations are several types of common, visually objectionable artifacts. These occur when scene geometry is approximate or viewpoints differ from the original shots, leading to parallax distortions, blurring, ghosting and popping errors that detract from the appearance of the scene. We argue that a better understanding of the causes and perceptual impact of these artifacts is the key to improving IBR methods. In this study we present a series of psychophysical experiments in which we systematically map out the perception of artifacts in IBR visualizations of façades as a function of the most common causes. We separate artifacts into different classes and measure how they impact visual appearance as a function of the number of images available, the geometry of the scene and the viewpoint. The results reveal a number of counter‐intuitive effects in the perception of artifacts. We summarize our results in terms of practical guidelines for improving existing and future IBR techniques.

Abstract We present a new solution for temporal coherence in non‐photorealistic rendering (NPR) of animations. Given the conflicting goals of preserving the 2D aspect of the style and the 3D scene motion, any such solution is a tradeoff. We observe that primitive‐based methods in NPR can be seen as texture‐based methods when using large numbers of primitives, leading to our key insight, namely that this process is similar to sparse convolution noise in procedural texturing. Consequently, we present a new primitive for NPR based on Gabor noise, that preserves the 2D aspect of noise, conveys the 3D motion of the scene, and is temporally continuous. We can thus use standard techniques from procedural texturing to create various styles, which we show for interactive NPR applications. We also present a user study to evaluate this and existing solutions, and to provide more insight in the trade‐off implied by temporal coherence. The results of the study indicate that maintaining coherent motion is important, but also that our new solution provides a good compromise between the 2D aspect of the style and 3D motion.

Visual observation is our principal source of information in determining the nature of objects, including shape, material or roughness. The physiological and cognitive processes that resolve visual input into an estimate of the material of an object are influenced by the illumination and the shape of the object. This affects our ability to select materials by observing them on a point-lit sphere, as is common in current 3D modeling applications. In this paper we present an exploratory psychophysical experiment to study various influences on material discrimination in a realistic setting. The resulting data set is analyzed using a wide range of statistical techniques. Analysis of variance is used to estimate the magnitude of the influence of geometry, and fitted psychometric functions produce significantly diverse material discrimination thresholds across different shapes and materials. Suggested improvements to traditional material pickers include direct visualization on the target object, environment illumination, and the use of discrimination thresholds as a step size for parameter adjustments.