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Image-based material editing neural networks have been trained on perceptual attributes because such attributes are human-friendly. But it seems that training such networks on non-perceptual material parameters has been neglected in comparison. It is interesting that collecting perceptual experiment data has been considered an acceptable additional effort until now. It would be much easier to generate a dataset with ground-truth material parameter attributes instead. Ground-truth parameters also avoid the noise that is inherent in perceptual experiment data. We show that existing neural networks can be trained on datasets with ground-truth material parameters and that they generate material edits of similar quality and that stay as close to the valid gamut of the trained material model as neural networks trained on perceptual material attributes. We expect that these results will encourage more study of the qualitative and quantitative differences between image-based material editing networks trained on material parameters and on perceptual attributes.

Point cloud registration plays a crucial role in many applications, from robotics and autonomous navigation to medical imaging and 3D scene reconstruction. While the Iterative Closest Point (ICP) algorithm is a well-known shape registration choice, its efficiency and accuracy can be affected by the vast search space for point correspondences. k-means clustering emerges as a promising solution for partitioning the search space into smaller clusters to reduce the computational complexity and increase the performance of the matching. However, the number and size of these clusters and how they affect the registration remains a critical and yet not fully explored factor. This paper delves into the relationship between the number of k-means clusters and point cloud registration accuracy. To determine the effect of the number of k-means clusters on registration accuracy and efficiency and to understand any emerging pattern, k-meansICP is developed to use the k-means algorithm to cluster the correspondence search space. Two sets of 3D molecular shapes with differing complexities are matched using initial rotation angles 15, 30, and 60 degrees with 2 to 10 k-means clusters. The results are then compared with the original ICP algorithm.

Subdivision is a method for generating a limit surface from a coarse mesh by recursively dividing its faces into several smaller faces. This process leads to smooth surfaces, but often suffers from shading artifacts near extraordinary points due to the lower quality of the normal field there. The idea of subdivision shading is to apply the same subdivision rules that are used to subdivide geometry to also subdivide the normals associated with mesh vertices. This leads to smoother normal fields, which in turn removes the shading artifacts. However, the original subdivision shading method does not support sharp and semi-sharp creases, which are important ingredients in subdivision surface modelling. We present two approaches to extending subdivision shading to work also on models with (semi-)sharp creases.

This paper uses the findings from a literature review and series of expert interviews to develop a richer and Purchasing and Supply Management (PSM) context-specific perspective of the different key techniques, tools and principles that can be used to develop gamified learning to enhance the skills required by PSM professionals in dealing with current and future challenges, such as the transformation to Industry 4.0. It also provides further details of the different stages of implementing gamified learning, which can enhance the success of any such provision.

Gamification has been used in a wide variety of subject-specific education contexts. Examples of such usage in the Supply Chain Management (SCM) context include the oft-played beer distribution game, developed by MIT Sloan School of Management (Forrester, 1961), which simulates the coordination of typical problems in supply chain processes, promoting information sharing and collaboration throughout a supply chain (Sterman, 1984). Purchasing and Supply Management (PSM), a subset of this wider SCM area, focuses on the direct relationships between organisational buyers and suppliers, covering aspects such as establishing trust, identifying and selecting suitable suppliers, managing supplier performance and the overall relationship. A systematic review of the PSM gamified learning literature establishes that there has been limited research to date and that which there is tends to focus on quantitative representations of managing overall supply and demand, using wider SCM elements. This suggests that there are opportunities to gamify PSM learning, in particular focusing on the human element in PSM and developing soft skills, as strong buyer-supplier relationships can generate significant benefits to both parties. To provide a more focused PSM contribution, a second systematic literature review distils the relevant principles, techniques and processes to inform the development of two gamified PSM learning activities. Negotiation and supplier relationship management rely heavily on personal interactions and are both seen as key activities at different stages of the PSM process. The development of the two gamified learning activities is strengthened by being underpinned by a synthesis of the literature review’s key findings, ensuring they are domain-meaningful abstractions of reality, contain rewards and rankings based on clear objectives and have appealing gameplay. It is hoped that this paper provides a platform for future domain specific PSM research and will be of use t

A Smart Contract (SC) is a program 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 smart contracts 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 smart contracts. 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.

Three-dimensional shapes are important in representing physical objects in digital form. This digital representation is useful in applications in numerous fields including chemistry, biology, and engineering. The benefits in analyzing and processing such 3D digital data have given rise to vast amounts of available 3D data and related applications. In recent times, techniques for determining the functional and structural relationships amongst proteins consider the whole 3D structure (spatial coordinates), proceeding from earlier techniques that were based on sequence information. However, techniques considering the 3D structure of all atomic positions of the protein are too demanding for fast similarity searches especially when the protein is made up of very large numbers of atoms. Iterative Closest Point (ICP) is the standard algorithm for performing 3D shape matching tasks. ICP has several issues that can affect the process of 3D shape matching. These include the need for an initial transformation to ensure an optimal match, inability of algorithm to converge when rotations are large, and computational cost and complexity of the distance calculation. We propose an improvement of the ICP algorithm, Tagged-ICP for matching 3D protein structures that takes into consideration known feature descriptions of the points. The search for correspondence in our algorithm matches atoms based on their meta data (atom types), making this approach more meaningful. Our algorithm also reduces the number of distance calculations by a factor depending on the partition. The neighbourhood information also increases the partitions and reduces the size of the search space even further. Our experimental results based on the publicly accessible Protein Data Bank show that matching becomes inherently meaningful and the complexity of the distance calculation is reduced. Our results also demonstrate improvements in

Overall, the mixed-reality CICO simulations that we developed appear to have created a simulation with an appropriate high level of fidelity, with immersion and authenticity. An important aspect of our simulations was that they also appeared to have sufficient fidelity to replicate the thought processes of real world experience. The recommendation to increase auditory fidelity through the addition of background noise has the potential to increase the level of immersion and authenticity, with the creation of escalating levels of stress. The optimum level of this additional auditory fidelity to supplement the visual fidelity is uncertain. Our findings from this feasibility study have encouraged us to continue to develop and research the use of mixed-reality simulation training for the management of CICO emergencies, with the further development of increased auditory fidelity and research on evoked stress and comparison across different scenarios. The study population was small but our findings have potential relevance to inform the development of similar future simulations in other contexts and to stimulate further research, especially since no similar studies were identified.

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.

Most previous work on gloss perception has examined the strength and sharpness of specular reflections in simple bidirectional reflectance distribution functions (BRDFs) having a single specular component. However, BRDFs can be substantially more complex and it is interesting to ask how many additional perceptual dimensions there could be in the visual representation of surface reflectance qualities. To address this, we tested materials with two specular components that elicit an impression of hazy gloss. Stimuli were renderings of irregularly shaped objects under environment illumination, with either a single Ward specular BRDF component (Ward, 1992), or two such components, with the same total specular reflectance but different sharpness parameters, yielding both sharp and blurry highlights simultaneously. Differently shaped objects were presented side by side in matching, discrimination, and rating tasks. Our results show that observers mainly attend to the sharpest reflections in matching tasks, but they can indeed discriminate between single-component and twocomponent specular materials in discrimination and rating tasks. The results reveal an additional perceptual dimension of gloss—beyond strength and sharpness— akin to “haze gloss” (Hunter & Harold, 1987). However, neither the physical measurements of Hunter and Harold nor the kurtosis of the specular term predict perception in our tasks. We suggest the visual system may use a decomposition of specular reflections in the perception of hazy gloss, and we compare two possible candidates: a physical representation made of two gloss components, and an alternative representation made of a central gloss component and a surrounding halo component.

A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDRVQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.

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.

Several approaches attempt to reproduce the appearance of a scotopic low-light night scene on a photopic display (“dayfor- night”) by introducing color desaturation, loss of acuity and the Purkinje shift towards 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.

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.