749,980 research outputs found
An Overview of Rendering from Volume Data --- including Surface and Volume Rendering
Volume rendering is a title often ambiguously used in science. One meaning often quoted is: `to render any three volume dimensional data set'; however, within this categorisation `surface rendering'' is contained. Surface rendering is a technique for visualising a geometric representation of a surface from a three dimensional volume data set. A more correct definition of Volume Rendering would only incorporate the direct visualisation of volumes, without the use of intermediate surface geometry representations. Hence we state: `Volume Rendering is the Direct Visualisation of any three dimensional Volume data set; without the use of an intermediate geometric representation for isosurfaces'; `Surface Rendering is the Visualisation of a surface, from a geometric approximation of an isosurface, within a Volume data set'; where an isosurface is a surface formed from a cross connection of data points, within a volume, of equal value or density. This paper is an overview of both Surface Rendering and Volume Rendering techniques. Surface Rendering mainly consists of contouring lines over data points and triangulations between contours. Volume rendering methods consist of ray casting techniques that allow the ray to be cast from the viewing plane into the object and the transparency, opacity and colour calculated for each cell; the rays are often cast until an opaque object is `hit' or the ray exits the volume
Drishti, a volume exploration and presentation tool
Among several rendering techniques for volumetric data, direct volume rendering is a powerful visualization tool for a wide variety of applications. This paper describes the major features of hardware based volume exploration and presentation tool - Drishti. The word, Drishti, stands for vision or insight in Sanskrit, an ancient Indian language. Drishti is a cross-platform open-source volume rendering system that delivers high quality, state of the art renderings. The features in Drishti include, though not limited to, production quality rendering, volume sculpting, multi-resolution zooming, transfer function blending, profile generation, measurement tools, mesh generation, stereo/anaglyph/crosseye renderings. Ultimately, Drishti provides an intuitive and powerful interface for choreographing animations
GPU-based Streaming for Parallel Level of Detail on Massive Model Rendering
Rendering massive 3D models in real-time has long been recognized as a very challenging problem because of the limited computational power and memory space available in a workstation. Most existing rendering techniques, especially level of detail (LOD) processing, have suffered from their sequential execution natures, and does not scale well with the size of the models. We present a GPU-based progressive mesh simplification approach which enables the interactive rendering of large 3D models with hundreds of millions of triangles. Our work contributes to the massive rendering research in two ways. First, we develop a novel data structure to represent the progressive LOD mesh, and design a parallel mesh simplification algorithm towards GPU architecture. Second, we propose a GPU-based streaming approach which adopt a frame-to-frame coherence scheme in order to minimize the high communication cost between CPU and GPU. Our results show that the parallel mesh simplification algorithm and GPU-based streaming approach significantly improve the overall rendering performance
Design of a multimodal rendering system
This paper addresses the rendering of aligned regular multimodal
datasets. It presents a general framework of multimodal data fusion
that includes several data merging methods. We also analyze the
requirements of a rendering system able to provide these different
fusion methods. On the basis of these requirements, we propose a novel
design for a multimodal rendering system. The design has been
implemented and proved showing to be efficient and flexible.Postprint (published version
Adaptive User Perspective Rendering for Handheld Augmented Reality
Handheld Augmented Reality commonly implements some variant of magic lens
rendering, which turns only a fraction of the user's real environment into AR
while the rest of the environment remains unaffected. Since handheld AR devices
are commonly equipped with video see-through capabilities, AR magic lens
applications often suffer from spatial distortions, because the AR environment
is presented from the perspective of the camera of the mobile device. Recent
approaches counteract this distortion based on estimations of the user's head
position, rendering the scene from the user's perspective. To this end,
approaches usually apply face-tracking algorithms on the front camera of the
mobile device. However, this demands high computational resources and therefore
commonly affects the performance of the application beyond the already high
computational load of AR applications. In this paper, we present a method to
reduce the computational demands for user perspective rendering by applying
lightweight optical flow tracking and an estimation of the user's motion before
head tracking is started. We demonstrate the suitability of our approach for
computationally limited mobile devices and we compare it to device perspective
rendering, to head tracked user perspective rendering, as well as to fixed
point of view user perspective rendering
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
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