Ray tracing has long been a method of choice for off-line rendering, but traditionally was too slow for interactive use. With faster hardware and algorithmic improvements this has recently changed, and real-time ray tracing is finally within reach. However, real-time capability also opens up new problems that do not exist in an off-line environment. In particular real-time ray tracing offers the opportunity to interactively ray trace moving/animated scene content.



This presents a challenge to the data structures that have been developed for ray tracing over the past few decades. Spatial data structures crucial for fast ray tracing must be rebuilt or updated as the scene changes, and this can become a bottleneck for the speed of ray tracing. This bottleneck has recently received much attention by researchers and that has resulted in a multitude of different algorithms, data structures and strategies for handling animated scenes. The effectiveness of techniques for ray tracing dynamic scenes vary dramatically depending on details such as scene complexity, model structure, type of motion and the coherency of the rays. Consequently, there is so far no approach that is best in all cases, and determining the best technique for a particular problem can be a challenge. In this State of the Art Report (STAR), we aim to survey the different approaches to ray tracing animated scenes, discussing their strengths and weaknesses, and their relationship to other approaches. The overall goal is to help the reader choose the best approach depending on the situation, and to expose promising areas where there is potential for algorithmic improvements.

/pdf/state-of-the-art-in-ray-tracing-animated-scenes-2ftu752sge.pdf

State of the Art in Ray Tracing Animated Scenes

Visualization in Medicine is the first book on visualization and its application to problems in medical diagnosis, education, and treatment. The book describes the algorithms, the applications and their validation (how reliable are the results?), and the clinical evaluation of the applications (are the techniques useful?). It discusses visualization techniques from research literature as well as the compromises required to solve practical clinical problems. 

The book covers image acquisition, image analysis, and interaction techniques designed to explore and analyze the data. The final chapter shows how visualization is used for planning liver surgery, one of the most demanding surgical disciplines. The book is based on several years of the authors' teaching and research experience. Both authors have initiated and lead a variety of interdisciplinary projects involving computer scientists and medical doctors, primarily radiologists and surgeons.

* A core field of visualization and graphics missing a dedicated book until now
* Written by pioneers in the field and illustrated in full color
* Covers theory as well as practice

Visualization in Medicine: Theory, Algorithms, and Applications

Virtual Medicine is an emerging and challenging field in Computer Graphics. Numerous visualization methods are used to model and render data of different modalities.

VIVENDI - A Virtual Ventricle Endoscopy System for Virtual Medicine

We present a data-parallel algorithm for identifying and labeling the connected sub-meshes within a domaindecomposed 3D mesh. The identification task is challenging in a distributed-memory parallel setting because connectivity is transitive and the cells composing each sub-mesh may span many or all processors. Our algorithm employs a multi-stage application of the Union-find algorithm and a spatial partitioning scheme to efficiently merge information across processors and produce a global labeling of connected sub-meshes. Marking each vertex with its corresponding sub-mesh label allows us to isolate mesh features based on topology, enabling new analysis capabilities. We briefly discuss two specific applications of the algorithm and present results from a weak scaling study. We demonstrate the algorithm at concurrency levels up to 2197 cores and analyze meshes containing up to 68 billion cells.

/pdf/data-parallel-mesh-connected-components-labeling-and-1a67ur31ll.pdf

Data-parallel mesh connected components labeling and analysis

The visualization of volumetric datasets is usually limited by the amount of memory and processing power of computer systems. Several multiresolution methods have been developed in order to adapt the necessary work; recursive spatial tree structures, such as octrees, are among the most popular. The exploration of a dataset frequently requires a change of parameters, such as color table entries or isovalues. Therefore, the costly update of an octree becomes necessary. To overcome this drawback, we propose the parallel construction of octrees to improve their suitability for interactive volume visualization. Based on the thread model of the shared-memory paradigm, we developed a scheme for a balanced parallel construction. We apply this new scheme to generate isosurfaces in parallel, using the Marching Cubes algorithm.

/pdf/parallel-construction-and-isosurface-extraction-of-recursive-xee2m1o7gp.pdf

Parallel construction and isosurface extraction of recursive tree structures

We present a procedure for the segmentation of the mastoid — a sponge-like bone structure behind the middle ear — from CT Images. This segmentation is a first pre-operative step for a robot-supported hearing aid implantation. Up to now, the mastoid is milled out by an ENT surgeon to allocate space for the hearing aid. This tedious milling process of the mastoid is intended to be performed by a robot to reduce the required time and improve the accuracy.

/pdf/segmenting-the-mastoid-allocating-space-in-the-head-for-a-2ok98ojzk5.pdf

Segmenting the Mastoid: Allocating Space in the Head for a Hearing Aid Implantation

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Parallel construction and isosurface extraction of recursive tree structures

Segmenting the Mastoid: Allocating Space in the Head for a Hearing Aid Implantation