On‐the‐Fly Power‐Aware Rendering

Power saving is a prevailing concern in desktop computers and, especially, in battery‐powered devices such as mobile phones. This is generating a growing demand for power‐aware graphics applications that can extend battery life, while preserving good quality. In this paper, we address this issue by presenting a real‐time power‐efficient rendering framework, able to dynamically select the rendering configuration with the best quality within a given power budget. Different from the current state of the art, our method does not require precomputation of the whole camera‐view space, nor Pareto curves to explore the vast power‐error space; as such, it can also handle dynamic scenes. Our algorithm is based on two key components: our novel power prediction model, and our runtime quality error estimation mechanism. These components allow us to search for the optimal rendering configuration at runtime, being transparent to the user. We demonstrate the performance of our framework on two different platforms: a desktop computer, and a mobile device. In both cases, we produce results close to the maximum quality, while achieving significant power savings.     

Towards realism in drawing areas of interest on architecture diagrams

Areas of interest (AOIs) are defined as groups of elements of system architecture diagrams that share some common property. Visualizing AOIs is a useful addition to plain diagrams, such as UML diagrams. Some methods have been proposed to automatically draw AOIs on UML diagrams. However, it is not clear whether actual users perceive the results of such methods to be better or worse as compared to human-drawn AOI, and what needs to be improved. We present here a process of studying and improving the perceived quality of computer-drawn AOI. For this, we conducted a qualitative evaluation that delivered insight in how users perceive the quality of computer-drawn AOIs as compared to hand-drawn diagrams. Following these results, we derived and implemented several improvements to an existing algorithm for computer-drawn AOIs. Next, we designed a distance metric to quantitatively compare different AOI drawings, and used this metric to show that our improved rendering algorithm creates drawings which are closer to (good) human drawings than the original rendering algorithm. We present here the results of the user evaluation, our improved algorithm for drawing AOIs, and the quantitative analysis performed to compare different drawings. The combined user evaluation, algorithmic improvements, and quantitative comparison method support our claim of having improved the perceived quality and understandability of AOI rendered on architecture diagrams.     

Interactive multiscale tensor reconstruction for multiresolution volume visualization

Large scale and structurally complex volume datasets from high-resolution 3D imaging devices or computational simulations pose a number of technical challenges for interactive visual analysis. In this paper, we present the first integration of a multiscale volume representation based on tensor approximation within a GPU-accelerated out-of-core multiresolution rendering framework. Specific contributions include (a) a hierarchical brick-tensor decomposition approach for pre-processing large volume data, (b) a GPU accelerated tensor reconstruction implementation exploiting CUDA capabilities, and (c) an effective tensor-specific quantization strategy for reducing data transfer bandwidth and out-of-core memory footprint. Our multiscale representation allows for the extraction, analysis and display of structural features at variable spatial scales, while adaptive level-of-detail rendering methods make it possible to interactively explore large datasets within a constrained memory footprint. The quality and performance of our prototype system is evaluated on large structurally complex datasets, including gigabyte-sized micro-tomographic volumes.     

User-Centric Learning and Evaluation of Interactive Segmentation Systems

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user??a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.     

Separating semantics from rendering: a scene graph based architecture for graphics applications

A large number of rendering and graphics applications developed in research and industry are based on scene graphs. Traditionally, scene graphs encapsulate the hierarchical structure of a complete 3D scene, and combine both semantic and rendering aspects. In this paper, we propose a clean separation of the semantic and rendering parts of the scene graph. This leads to a generally applicable architecture for graphics applications that is loosely based on the well-known Model-View-Controller (MVC) design pattern for separating the user interface and computation parts of an application. We explore the benefits of this new design for various rendering and modeling tasks, such as rendering dynamic scenes, out-of-core rendering of large scenes, generation of geometry for trees and vegetation, and multi-view rendering. Finally, we show some of the implementation details that have been solved in the process of using this software architecture in a large framework for rapid development of visualization and rendering applications.     

Tarsier and ICMS: two approaches to framework development

Modelling frameworks provide models with support components that handle tasks such as visualisation, data management and model integration. Within these broad requirements different approaches to framework development are possible. Tarsier is a modelling framework that supports the development of models in a high-level language, such as C++. This approach allows Tarsier model developers to craft object oriented solutions to large modelling problems. ICMS is a software system that supports the development of models in a custom modelling language that allows modellers with little programming experience to develop, integrate and visualise catchment models. Both frameworks provide sophisticated tools for model linking, data management, and data analysis and visualisation. By focusing on different user groups, Tarsier and ICMS have evolved into quite different environments, yet both satisfy the definition of a modelling framework. This paper concentrates on the components within each framework and the strengths and weaknesses of the different approaches.     

Perception-based fast rendering and antialiasing of walkthroughsequences

We consider accelerated rendering of high quality walkthrough animation sequences along predefined paths. To improve rendering performance, we use a combination of a hybrid ray tracing and image-based rendering (IBR) technique and a novel perception-based antialiasing technique. In our rendering solution, we derive as many pixels as possible using inexpensive IBR techniques without affecting the animation quality. A perception-based spatiotemporal animation quality metric (AQM) is used to automatically guide such a hybrid rendering. The image flow (IF) obtained as a byproduct of the IBR computation is an integral part of the AQM. The final animation quality is enhanced by an efficient spatiotemporal antialiasing which utilizes the IF to perform a motion-compensated filtering. The filter parameters have been tuned using the AQM predictions of animation quality as perceived by the human observer. These parameters adapt locally to the visual pattern velocity     

Integrating Occlusion Culling and Levels of Detail through Hardly-Visible Sets

Occlusion culling and level-of-detail rendering have become two powerful tools for accelerating the handling of very large models in real-time visualization applications. We present a framework that combines both techniques to improve rendering times. Classical occlusion culling algorithms compute potentially visible sets (PVS), which are supersets of the sets of visible polygons. The novelty of our approach is to estimate the degree of visibility of each object of the PVS using synthesized coarse occluders. This allows to arrange the objects of each PVS into several Hardly-Visible Sets (HVS) with similar occlusion degree. According to image accuracy and frame rate requirements, HVS provide a way to avoid sending to the graphics pipeline those objects whose pixel contribution is low due to partial occlusion. The image error can be bounded by the user at navigation time. On the other hand, as HVS offer a tighter estimation of the pixel contribution for each scene object, it can be used for a more convenient selection of the level-of-detail at which objects are rendered. In this paper, we describe the new framework technique, provide details of its implementation using a visibility octree as the chosen occlusion culling data structure and show some experimental results on the image quality.     

Progressive volume rendering of large unstructured grids

We describe a new progressive technique that allows real-time rendering of extremely large tetrahedral meshes. Our approach uses a client-server architecture to incrementally stream portions of the mesh from a server to a client which refines the quality of the approximate rendering until it converges to a full quality rendering. The results of previous steps are re-used in each subsequent refinement, thus leading to an efficient rendering. Our novel approach keeps very little geometry on the client and works by refining a set of rendered images at each step. Our interactive representation of the dataset is efficient, light-weight, and high quality. We present a framework for the exploration of large datasets stored on a remote server with a thin client that is capable of rendering and managing full quality volume visualizations.     

A Fast Ambient Occlusion Method for Real-Time Plant Rendering

Global illumination effects are crucial for virtual plant rendering. Whereas real-time global illumination rendering of plants is impractical, ambient occlusion is an efficient alternative approximation. A tree model with millions of triangles is common, and the triangles can be considered as randomly distributed. The existing ambient occlusion methods fail to apply on such a type of object. In this paper, we present a new ambient occlusion method dedicated to real time plant rendering with limited user interaction. This method is a three-step ambient occlusion calculation framework which is suitable for a huge number of geometry objects distributed randomly in space. The complexity of the proposed algorithm is O（n）, compared to the conventional methods with complexities of O（n^2）. Furthermore, parameters in this method can be easily adjusted to achieve flexible ambient occlusion effects. With this ambient occlusion calculation method, we can manipulate plant models with millions of organs, as well as geometry objects with large number of randomly distributed components with affordable time, and with perceptual quality comparable to the previous ambient occlusion methods.     

Improved Model‐ and View‐Dependent Pruning of Large Botanical Scenes

We present an optimized pruning algorithm that allows for considerable geometry reduction in large botanical scenes while maintaining high and coherent rendering quality. We improve upon previous techniques by applying model‐specific geometry reduction functions and optimized scaling functions. For this we introduce the use of Precision and Recall (PR) as a measure of quality to rendering and show how PR‐scores can be used to predict better scaling values. We conducted a user‐study letting subjects adjust the scaling value, which shows that the predicted scaling matches the preferred ones. Finally, we extend the originally purely stochastic geometry prioritization for pruning to account for view‐optimized geometry selection, which allows to take global scene information, such as occlusion, into consideration. We demonstrate our method for the rendering of scenes with thousands of complex tree models in real‐time.     

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.     

A systematic framework for evaluating design concepts of a new product

This study aims to suggest a systematic framework to evaluate design concepts for a new product at the concept‐development phase. It focuses especially on evaluating design concepts based on user requirements and implicit tasks, defining trends in technology alternatives, and relating users' perceived value to product functionality. The potential user needs and functional requirements were identified through scenario‐based analysis and hierarchical task analysis. Technology alternatives were also investigated to support users in performing the required tasks effectively and efficiently. For a quantifiable evaluation measure, customer‐perceived value (CPV) attributes were used to evaluate the benefits and costs of the current design concept as compared to perceived alternatives. A case study was conducted to evaluate the design concepts of a new computer‐supported cooperative work (CSCW)‐based system with a tangible user interface, which was designed to support group decision‐making activities, such as business meetings. At the concept‐development phase, engineering specifications are not determined, and cost cannot be precisely estimated. Thus, while we dealt with design‐concept evaluation, we had no choice but to exclude cost attributes, such as monetary expenditure. It is still expected that our framework would be effective in incorporating user‐centered design perspectives early in the process of new product development. © 2010 Wiley Periodicals, Inc.     

Prototyping and analysing ubiquitous computing environments using multiple layers

If ubiquitous computing (ubicomp) is to enhance physical environments then early and accurate assessment of alternative solutions will be necessary to avoid costly deployment of systems that fail to meet requirements. This paper presents APEX, a prototyping framework that combines a 3D Application Server with a behaviour modeling tool. The contribution of this framework is that it allows exhaustive analysis of the behaviour models that drive the prototype while at the same time enabling immersive exploration of a virtual environment simulating the proposed system. The development of prototypes is supported through three layers: a simulation layer (using OpenSimulator); a modelling layer (using CPN Tools) and a physical layer (using external devices and real users). APEX allows movement between these layers to analyse different features, from user experience to user behaviour. The multi layer approach makes it possible to express user behaviour in the modelling layer, provides a way to reduce the number of real users needed by adding simulated avatars, and supports user testing of hybrids of virtual and real components as well as exhaustive analysis. This paper demonstrates the approach by means of an example, placing particular emphasis on the simulation of virtual environments, low cost prototyping and the formal analysis capabilities.     

Stroke Style Analysis for Painterly Rendering

We propose a novel method that automatically analyzes stroke-related artistic styles of paintings.A set of adaptive interfaces are also developed to connect the style analysis with existing painterly rendering systems, so that the specific artistic style of a template painting can be effectively transferred to the input photo with minimal effort.Different from conventional texture-synthesis based rendering techniques that focus mainly on texture features, this work extracts, analyzes and simulates high-level style features expressed by artists’ brush stroke techniques.Through experiments, user studies and comparisons with ground truth, we demonstrate that the proposed style-orientated painting framework can significantly reduce tedious parameter adjustment, and it allows amateur users to efficiently create desired artistic styles simply by specifying a template painting.     

Transform coding for hardware-accelerated volume rendering

Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.     

Mobile computing: a user study on hedonic/utilitarian mobile device usage

Intrinsic motivators of technology beliefs have received scant attention in the technology acceptance literature despite indications of their efficacy. This study uses the framework of TAM to explore the effect of intrinsic variables on technology beliefs and user behavior. Specifically, we examine the effect of cognitive absorption and playfulness on user beliefs including perceived enjoyment and perceived usefulness within the context of mobile devices. Moreover, we manipulate the hedonic and utilitarian purpose of the mobile device to determine how the nature of the device influences user beliefs. Findings indicate that cognitive absorption and user playfulness significantly impact beliefs and that the hedonic or utilitarian orientation of the technology has implications for maximizing use.     

Render2MPEG: A Perception-based Framework Towards Integrating Rendering and Video Compression

Currently 3D animation rendering and video compression are completely independent processes even if rendered frames are streamed on‐the‐fly within a client‐server platform. In such scenario, which may involve time‐varying transmission bandwidths and different display characteristics at the client side, dynamic adjustment of the rendering quality to such requirements can lead to a better use of server resources. In this work, we present a framework where the renderer and MPEG codec are coupled through a straightforward interface that provides precise motion vectors from the rendering side to the codec and perceptual error thresholds for each pixel in the opposite direction. The perceptual error thresholds take into account bandwidth‐dependent quantization errors resulting from the lossy com‐pression as well as image content‐dependent luminance and spatial contrast masking. The availability of the discrete cosine transform (DCT) coefficients at the codec side enables to use advanced models of the human visual system (HVS) in the perceptual error threshold derivation without incurring any significant cost. Those error thresholds are then used to control the rendering quality and make it well aligned with the compressed stream quality. In our prototype system we use the lightcuts technique developed by Walter et al., which we enhance to handle dynamic image sequences, and an MPEG‐2 implementation. Our results clearly demonstrate many advantages of coupling the rendering with video compression in terms of faster rendering. Furthermore, temporally coherent rendering leads to a reduction of temporal artifacts.     

A sorting classification of parallel rendering

We describe a classification scheme that we believe provides a more structured framework for reasoning about parallel rendering. The scheme is based on where the sort from object coordinates to screen coordinates occurs, which we believe is fundamental whenever both geometry processing and rasterization are performed in parallel. This classification scheme supports the analysis of computational and communication costs, and encompasses the bulk of current and proposed highly parallel renderers - both hardware and software. We begin by reviewing the standard feed-forward rendering pipeline, showing how different ways of parallelizing it lead to three classes of rendering algorithms. Next, we consider each of these classes in detail, analyzing their aggregate processing and communication costs, possible variations, and constraints they may impose on rendering applications. Finally, we use these analyses to compare the classes and identify when each is likely to be preferable