Smart energy technologies provide new opportunities for network demand management, while generating data that can provide inference into household activities and which is increasingly valuable for user profiling and targeted marketing. This paper explores the human values implicated by smart energy technologies as energy consumption emerges as a potentially sensitive and attributable data trail. Using a values-based approach involving semi-structured interviews with 39 Australians, we use speculative design to engage participants with the implications of high frequency household energy data and understand users’ values and attitudes towards curtailment, surveillance, accountability and consent. The principal contributions are (1) a nuanced understanding of user values around privacy, responsibility, trust, collectivism and curtailment in relation to interpersonal household energy use data and considerations for speculative designs in this space, and (2) reflections on the design of mechanisms for consumer participation in demand response for energy networks.

Material appearance of rendered objects depends on the underlying BRDF implementation used by rendering software packages. A lack of standards to exchange material parameters and data (between tools) means that artists in digital 3D prototyping and design, manually match the appearance of materials to a reference image. Since their effect on rendered output is often non-uniform and counter intuitive, selecting appropriate parameterisations for BRDF models is far from straightforward. We present a novel BRDF remapping technique, that automatically computes a mapping (BRDF Difference Probe) to match the appearance of a source material model to a target one. Through quantitative analysis, four user studies and psychometric scaling experiments, we validate our remapping framework and demonstrate that it yields a visually faithful remapping among analytical BRDFs. Most notably, our results show that even when the characteristics of the models are substantially different, such as in the case of a phenomenological model and a physically-based one, our remapped renderings are indistinguishable from the original source model.

Photorealistic and physically-based rendering of real-world environments with high fidelity materials is important to a range of applications, including special effects, architectural modelling, cultural heritage, computer games, automotive design, and virtual reality (VR). Our perception of the world depends on lighting and surface material characteristics, which determine how the light is reflected, scattered, and absorbed. In order to reproduce appearance, we must therefore understand all the ways objects interact with light, and the acquisition and representation of materials has thus been an important part of computer graphics from early days. Nevertheless, no material model nor acquisition setup is without limitations in terms of the variety of materials represented, and different approaches vary widely in terms of compatibility and ease of use.

We present a fast, novel image-based technique for reverse engineering woven fabrics at a yarn level. These models can be used in a wide range of interior design and visual special effects applications. To recover our pseudo-Bidirectional Texture Function (BTF), we estimate the three-dimensional (3D) structure and a set of yarn parameters (e.g., yarn width, yarn crossovers) from spatial and frequency domain cues. Drawing inspiration from previous work [Zhao et al. 2012], we solve for the woven fabric pattern and from this build a dataset. In contrast, however, we use a combination of image space analysis and frequency domain analysis, and, in challenging cases, match image statistics with those from previously captured known patterns. Our method determines, from a single digital image, captured with a digital single-lens reflex (DSLR) camera under controlled uniform lighting, the woven cloth structure, depth, and albedo, thus removing the need for separately measured depth data. The focus of this work is on the rapid acquisition of woven cloth structure and therefore we use standard approaches to render the results. Our pipeline first estimates the weave pattern, yarn characteristics, and noise statistics using a novel combination of low-level image processing and Fourier analysis. Next, we estimate a 3D structure for the fabric sample using a first-order Markov chain and our estimated noise model as input, also deriving a depth map and an albedo. Our volumetric textile model includes information about the 3D path of the center of the yarns, their variable width, and hence the volume occupied by the yarns, and colors. We demonstrate the efficacy of our approach through comparison images of test scenes rendered using (a) the original photograph, (b) the segmented image, (c) the estimated weave pattern, and (d) the rendered result.

Abstract Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real‐world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re‐produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. This survey of the state‐of‐the‐art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials.

The prospect of computers or machines understanding the emotional state of a user is one that has been around for nearly half a century. Even so, it was only 1995 when Picard coined the term 'Affective Computing' [1] and a research field was born. A community now exists where researchers work towards understanding the concept of emotion and how computers can collect and understand the emotional state of its users. Even though techniques and technologies have advanced to a level where the goal is possible, a lack of common direction and too much focus on success measures within the community is preventing the goal from being realised. The purpose of this poster is to present a new approach to affective computing based on community collaboration and the use of methods that are successful within other more successful fields.

Abstract We present a novel method to estimate an approximation of the reflectance characteristics of optically thick, homogeneous translucent materials using only a single photograph as input. First, we approximate the diffusion profile as a linear combination of piecewise constant functions, an approach that enables a linear system minimization and maximizes robustness in the presence of suboptimal input data inferred from the image. We then fit to a smoother monotonically decreasing model, ensuring continuity on its first derivative. We show the feasibility of our approach and validate it in controlled environments, comparing well against physical measurements from previous works. Next, we explore the performance of our method in uncontrolled scenarios, where neither lighting nor geometry are known. We show that these can be roughly approximated from the corresponding image by making two simple assumptions: that the object is lit by a distant light source and that it is globally convex, allowing us to capture the visual appearance of the photographed material. Compared with previous works, our technique offers an attractive balance between visual accuracy and ease of use, allowing its use in a wide range of scenarios including off‐the‐shelf, single images, thus extending the current repertoire of real‐world data acquisition techniques.

Retrieving information presented visually is difficult for visually disabled users. Current accessibility technologies, such as screen readers, fail to convey presentational layout or structure. Information presented in graphs or images is almost impossible to convey through speech alone. In this paper, we present the results of an experimental study investigating the role of touch (haptic) and auditory cues in aiding structure recognition when visual presentation is missing. We hypothesize that by guiding users toward nodes in a graph structure using force fields, users will find it easier to recognize overall structure. Nine participants were asked to explore simple 3D structures containing nodes (spheres or cubes) laid out in various spatial configurations and asked to identify the nodes and draw their overall structure. Various combinations of haptic and auditory feedback were explored. Our results demonstrate that haptic cues significantly helped participants to quickly recognize nodes and structure. Surprisingly, auditory cues alone did not speed up node recognition; however, when they were combined with haptics both node identification and structure recognition significantly improved. This result demonstrates that haptic feedback plays an important role in enabling people to recall spatial layout.

Capturing detailed surface geometry currently requires specialized equipment such as laser range scanners, which despite their high accuracy, leave gaps in the surfaces that must be reconciled with photographic capture for relighting applications. Using only a standard digital camera and a single view, we present a method for recovering models of predominantly diffuse textured surfaces that can be plausibly relit and viewed from any angle under any illumination. Our multiscale shape-from-shading technique uses diffuse-lit/flash-lit image pairs to produce an albedo map and textured height field. Using two lighting conditions enables us to subtract one from the other to estimate albedo. In the absence of a flash-lit image of a surface for which we already have a similar exemplar pair, we approximate both albedo and diffuse shading images using histogram matching. Our depth estimation is based on local visibility. Unlike other depth-from-shading approaches, all operations are performed on the diffuse shading image in image space, and we impose no constant albedo restrictions. An experimental validation shows our method works for a broad range of textured surfaces, and viewers are frequently unable to identify our results as synthetic in a randomized presentation. Furthermore, in side-by-side comparisons, subjects found a rendering of our depth map equally plausible to one generated from a laser range scan. We see this method as a significant advance in acquiring surface detail for texturing using a standard digital camera, with applications in architecture, archaeological reconstruction, games and special effects.

We present a system that enables, for the first time, effective transatlantic cooperative haptic manipulation of objects whose motion is computed using a physically-based model. We propose a technique for maintaining synchrony between simulations in a peer-to-peer system, while providing responsive direct manipulation for all users. The effectiveness of this approach is determined through extensive user trials involving concurrent haptic manipulation of a shared object. A CAD assembly task, using physically-based motion simulation and haptic feedback, was carried out between the USA and the UK with network latencies in the order of 120ms. We compare the effects of latency on synchrony between peers over the Internet with a low latency (0.5ms) local area network. Both quantitatively and qualitatively, when using our technique, the performance achieved over the Internet is comparable to that on a LAN. As such, this technique constitutes a significant step forward for distributed haptic collaboration

Collaborative virtual environments (CVEs) enable two or more people, separated in the real world, to share the same virtual “space.” They can be used for many purposes, from teleconferencing to training people to perform assembly tasks. Unfortunately, the effectiveness of CVEs is compromised by one major problem: the delay that exists in the networks linking users together. Whilst we have a good understanding, especially in the visual modality, of how users are affected by delayed feedback from their own actions, little research has systematically examined how users are affected by delayed feedback from other people, particularly in environments that support haptic (force) feedback. The current study addresses this issue by quantifying how increasing levels of latency affect visual and haptic feedback in a collaborative target acquisition task. Our results demonstrate that haptic feedback in particular is very sensitive to low levels of delay. Whilst latency affects visual feedback from 50 ms, it impacts on haptic task performance 25 ms earlier, and causes the haptic measures of performance deterioration to rise far more steeply than visual. The “impact-perceive-adapt” model of user performance, which considers the interaction between performance measures, perception of latency, and the breakdown of perception of immediate causality, is proposed as an explanation for the observed pattern of performance.

Network architectures for collaborative virtual reality have traditionally been dominated by client-server and peer-to-peer approaches, with peer-to-peer strategies typically being favored where minimizing latency is a priority, and client-server where consistency is key. With increasingly sophisticated behavior models and the demand for better support for haptics, we argue that neither approach provides sufficient support for these scenarios and, thus, a hybrid architecture is required. We discuss the relative performance of different distribution strategies in the face of real network conditions and illustrate the problems they face. Finally, we present an architecture that successfully meets many of these challenges and demonstrate its use in a distributed virtual prototyping application which supports simultaneous collaboration for assembly, maintenance, and training applications utilizing haptics.

Physically-based modelling is a powerful technique for specifying the laws and characteristics of virtual environments (VEs) and the objects they contain. In this paper, we describe Iota, a component framework for creating and interacting with multi-body physically-based models in VEs. In conclusion, we present two case studies implemented within the framework.