XOR-Based Compact Triangulations

Media, image processing, and geometric-based systems and applications need data structures to model and represent different geometric entities and objects. These data structures have to be time efficient and compact in term of space. Many structures in use are proposed to satisfy those constraints. This paper introduces a novel compact data structure inspired by the XOR-linked lists. The subject of this paper concerns the triangular data structures. Nevertheless, the underlying idea could be used for any other geometrical subdivision. The ability of the bitwise XOR operator to reduce the number of references is used to model triangle and vertex references. The use of the XOR combined references needs to define a context from which the triangle is accessed. The direct access to any triangle is not possible using only the XOR-linked scheme. To allow the direct access, additional information are added to the structure. This additional information permits a constant time access to any element of the triangulation using a local resolution scheme. This information represents an additional cost to the triangulation, but the gain is still maintained. This cost is reduced by including this additional information to a local sub-triangulation and not to each triangle. Sub-triangulations are calculated implicitly according to the catalog-based structure. This approach could be easily extended to other representation models, such as vertex-based structures or edge-based structures. The obtained results are very interesting since the theoretical gain is estimated to 38 % and the practical gain obtained from sample benches is about 34 %

A Perceptually Based Comparison of Image Similarity Metrics

The assessment of how well one image matches another forms a critical component both of models of human visual processing and of many image analysis systems. Two of the most commonly used norms for quantifying image similarity are L1 and L2, which are specific instances of the Minkowski metric. However, there is often not a principled reason for selecting one norm over the other. One way to address this problem is by examining whether one metric, better than the other, captures the perceptual notion of image similarity. This can be used to derive inferences regarding similarity criteria the human visual system uses, as well as to evaluate and design metrics for use in image-analysis applications. With this goal, we examined perceptual preferences for images retrieved on the basis of the L1 versus the L2 norm. These images were either small fragments without recognizable content, or larger patterns with recognizable content created by vector quantization. In both conditions the participants showed a small but consistent preference for images matched with the L1 metric. These results suggest that, in the domain of natural images of the kind we have used, the L1 metric may better capture human notions of image similarity

DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact. We draw inspiration from previous work to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications

New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows

ORC Layout: Adaptive GUI Layout with OR-Constraints

We propose a novel approach for constraint-based graphical user interface (GUI) layout based on OR-constraints (ORC) in standard soft/hard linear constraint systems. ORC layout unifies grid layout and flow layout, supporting both their features as well as cases where grid and flow layouts individually fail. We describe ORC design patterns that enable designers to safely create flexible layouts that work across different screen sizes and orientations. We also present theORC Editor, a GUI editor that enables designers to apply ORC in a safe and effective manner, mixing grid, flow and new ORC layout features as appropriate. We demonstrate that our prototype can adapt layouts to screens with different aspect ratios with only a single layout specification, easing the burden of GUI maintenance. Finally, we show that ORC specifications can be modified interactively and solved efficiently at runtime

The Effect of Environmental Features, Self-Avatar, and Immersion on Object Location Memory in Virtual Environments

One potential application for virtual environments (VEs) is the training of spatial knowledge. A critical question is what features the VE should have in order to facilitate this training. Previous research has shown that people rely on environmental features, such as sockets and wall decorations, when learning object locations. The aim of this study is to explore the effect of varied environmental feature fidelity of VEs, the use of self-avatars, and the level of immersion on object location learning and recall. Following a between-subjects experimental design, participants were asked to learn the location of three identical objects by navigating one of the three environments: a physical laboratory or low and high detail VE replicas of this laboratory. Participants who experienced the VEs could use either a head-mounted display (HMD) or a desktop computer. Half of the participants learning in the HMD and desktop systems were assigned a virtual body. Participants were then asked to place physical versions of the three objects in the physical laboratory in the same configuration. We tracked participant movement, measured object placement, and administered a questionnaire related to aspects of the experience. HMD learning resulted in statistically significant higher performance than desktop learning. Results indicate that, when learning in low detail VEs, there is no difference in performance between participants using HMD and desktop systems. Overall, providing the participant with a virtual body had a negative impact on performance. Preliminary inspection of navigation data indicates that spatial learning strategies are different in systems with varying levels of immersion

Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects

The main purpose of this tutorial is to address theoretical and practical issues involved in the development of predictive material appearancemodels for interdisciplinary applications within and outside the visible spectral domain. We examine the specific constraints and pitfalls found in each of the key stages of the model development framework, namely data collection, design and evaluation, and discuss alternatives to enhance the effectiveness of the entire process. Although predictive material appearance models developed by computer graphics researchers are usually aimed at realistic image synthesis applications, they also provide valuable support for a myriad of advanced investigations in related areas, such as computer vision, image processing and pattern recognition, which rely on the accurate analysis and interpretation of material appearance attributes in the hyperspectral domain. In fact, their scope of contributions goes beyond the realm of traditional computer science applications. For example, predictive light transport simulations, which are essential for the development of these models, are also regularly beingused by physical and life science researchers to understand andpredict material appearance changes prompted by mechanisms which cannot be fully studied using standard ``wet'' experimental procedures.For completeness, this tutorial also provides an overview of such synergistic research efforts and in silico investigations, which are illustrated by case studies involving the use of hyperspectral material appearance models

Digitizing the chemical senses: possibilities & pitfalls

Many people are understandably excited by the suggestion that the chemical senses can be digitized; be it to deliver ambient fragrances (e.g., in virtual reality or health-related applications), or else to transmit flavour experiences via the internet. However, to date, progress in this area has been surprisingly slow. Furthermore, the majority of the attempts at successful commercialization have failed, often in the face of consumer ambivalence over the perceived benefits/utility. In this review, with the focus squarely on the domain of Human-Computer Interaction (HCI), we summarize the state-of-the-art in the area. We highlight the key possibilities and pitfalls as far as stimulating the so-called ‘lower’ senses of taste, smell, and the trigeminal system are concerned. Ultimately, we suggest that mixed reality solutions are currently the most plausible as far as delivering (or rather modulating) flavour experiences digitally is concerned. The key problems with digital fragrance delivery are related to attention and attribution. People often fail to detect fragrances when they are concentrating on something else; And even when they detect that their chemical senses have been stimulated, there is always a danger that they attribute their experience (e.g., pleasure) to one of the other senses – this is what we call ‘the fundamental attribution error’. We conclude with an outlook on digitizing the chemical senses and summarize a set of open-ended questions that the HCI community has to address in future explorations of smell and taste as interaction modalities