We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.

/pdf/kinectfusion-real-time-dense-surface-mapping-and-tracking-pl8e8pbmre.pdf

KinectFusion: Real-time dense surface mapping and tracking

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

/pdf/imagej2-imagej-for-the-next-generation-of-scientific-image-41ybjcosb6.pdf

ImageJ2: ImageJ for the next generation of scientific image data

Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm is able to join unconnected regions of models. This can facilitate much better approximations, both visually and with respect to geometric error. In order to allow topological joining, our system also supports non-manifold surface models. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—surface and object representations

/pdf/surface-simplification-using-quadric-error-metrics-r9zm14jwq8.pdf

Surface simplification using quadric error metrics

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. 
We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest.

/pdf/a-survey-of-general-purpose-computation-on-graphics-hardware-2phx1q9xxr.pdf

A Survey of General-Purpose Computation on Graphics Hardware

Most direct volume renderings produced today employ 1D transfer functions which assign color and opacity to the volume based solely on the single scalar quantity which comprises the data set. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data. However, identifying good transfer functions is difficult enough in 1D, let alone 2D or 3D. This paper demonstrates an important class of 3D transfer functions for scalar data, and describes the application of multi-dimensional transfer functions to multivariate data. We present a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to both interactively render with multidimensional transfer functions and to provide interactive shadows for volumes. The transfer functions, widgets and hardware combine to form a powerful system for interactive volume exploration.

Multidimensional transfer functions for interactive volume rendering

Visualization Handbook Section One: Introduction by Johnson and Hansen Chapter 1. Overview of Visualization by Schroeder Section Two: Scalar Field Visualization - Isosurfaces Chapter 1. Accelerated Isosurface Extraction Approaches by Livnat Chapter 2. Time Dependent Isosurface Extraction by Shen Chapter 3. Optimal Isosurface Extraction by Scopigno, Cignoni, Montani and Puppo Chapter 4. Isosurface Extraction using Extrema Graphs by Koyamada and Takayuki Chapter 5. Isosurfaces and Level-Sets by Whitaker Section Three: Scalar Field Visualization Volume Rendering Chapter 1. Overview of Volume Rendering by Arie Kaufman and Klaus Mueller Chapter 2. Volume Rendering using Splatting by Crawfis, Xue and Zhang Chapter 3. Multi-Dimensional Transfer Functions for Volume Rendering by Kniss, Kindlemann and Hansen Chapter 4. Preintegrated Volume Rendering by Kraus and Ertl Chapter 5. Hardware-Accelerated Volume Rendering by Pfister Section Four: Vector Field Visualization Chapter 1. Flow Visualization Overview by Weiskopf and Erlebacher Chapter 2. Flow Textures by Gordon Erlebacher, Jobard and Weiskopf Chapter 3. Detection and Visualization of Vortices by Jiang, Machiraju, and Thompson Section Five: Tensor Field Visualization Chapter 1. Oriented Tensor Reconstruction by Leonid Zhukov and Alan H. Barr Chapter 2. Diffusion Tensor MRI Visualization by Zang, Kindlemann and Laidlaw Chapter 3. Topological Methods for Tensor Visualization by Scheuermann and Tricoche Section Six: Geometric Modeling for Visualization Chapter 1. 3D Mesh Compression by Rossignac Chapter 2. Variational Modeling Methods for Visualization by Hagen and Hotz Chapter 3. Model Simplification by Cohen and Manocha Section Seven: Virtual Environments for Visualization Chapter 1. Direct Manipulation in Virtual Reality by Bryson Chapter 2. The Visual Haptic Workbench by Ikits and Brederson Chapter 3. Virtual Geographic Information Systems by Ribarsky Chapter 4. Visualization Using Virtual Reality by Loften, Chen and Rosenblum Section Eight: Large-scale Data Visualization Chapter 1. A Desktop Delivery: Access to Large Data Sets by Heermann and Pavlakos Chapter 2. Techniques for Visualizing Time-Varying Volume Data by Ma and Lum Chapter 3. Large Scale Data Visualization and Rendering: A Problem Driven Approach by McCormick and Ahrens Chapter 4. Issues and Architectures for Large Data Visualization by Pavlakos and Heermann Chapter 5. Consuming Network Bandwidth with Visapult by Bethel and Shalf Section Nine: Visualization Software and Frameworks Chapter 1. VTK The Visualization Toolkit by Schroeder and Martin Chapter 2. Visualization in the SCIRun Problem Solving Environment by Parker et al. Chapter 3. NAGs Iris Explorer by Walton Chapter 4. AVS and AVS Express by Favre and Valle Chapter 5. Vis5D, Cave5D and VisAD by Hibbard Chapter 6. Visualization with AVS by Manchester Visualization Center Chapter 7. ParaView by Ahrens, Geveci and Law Chapter 8. The Insight Toolkit (ITK) by Yoo Chapter 9. Amira- a Highly-interation system for Visual Data Analysis by Stalling, Westerhoff, Hege Section Ten: Perceptual Issues in Visualization Chapter 1. Extending Visualization to Perception: The Importance of Perception in Effective Communication of Information by Ebert Chapter 2. Art and Science Visualization by Interrante Chapter 3. Exploiting Human Visual Perception in Visualization by Chalmers and Cater Section Eleven: Selected Topics and Applications Chapter 1. Scalable Network Visualization by Eick Chapter 2. Visual Data Mining Techniques by Keim, Sips, Ankerst Chapter 3. Visualization in Weather and Climate Research by Middleton and Wilhelmson Chapter 4. Painting and Visualization by Kirby, Keefe, and Laidlaw Chapter 5. Visualization and Natural Control Systems for Microscopy by Taylor, Borland, Brooks , Falvo et al. Chapter 6. Visualization for Computational Accelerator Physics by Ma, Schussonan, Wilson

The Visualization Handbook

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.

Visual comparison for information visualization

Most direct volume renderings produced today employ one-dimensional transfer functions, which assign color and opacity to the volume based solely on the single scalar quantity which comprises the dataset. Though they have not received widespread attention, multi-dimensional transfer functions are a very effective way to extract specific material boundaries and convey subtle surface properties. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data (based on data value, gradient magnitude, and a second directional derivative), and describes a set of direct manipulation widgets which make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to interactively render with multi-dimensional transfer functions. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration.

/pdf/interactive-volume-rendering-using-multi-dimensional-14stozsp4s.pdf

Charles Hansen

Papers

Multidimensional transfer functions for interactive volume rendering

The Visualization Handbook

Visual comparison for information visualization

Interactive volume rendering using multi-dimensional transfer functions and direct manipulation widgets

Visualization and computer graphics