The authors develop a unique CT simulation tool based on the 4D extended cardiac-torso (XCAT) phantom, a whole-body computer model of the human anatomy and physiology based on NURBS surfaces Unlike current phantoms in CT based on simple mathematical primitives, the 4D XCAT provides an accurate representation of the complex human anatomy and has the advantage, due to its design, that its organ shapes can be changed to realistically model anatomical variations and patient motion A disadvantage to the NURBS basis of the XCAT, however, is that the mathematical complexity of the surfaces makes the calculation of line integrals through the phantom difficult They have to be calculated using iterative procedures; therefore, the calculation of CT projections is much slower than for simpler mathematical phantoms To overcome this limitation, the authors used efficient ray tracing techniques from computer graphics, to develop a fast analytic projection algorithm to accurately calculate CT projections directly from the surface definition of the XCAT phantom given parameters defining the CT scanner and geometry Using this tool, realistic high-resolution 3D and 4D projection images can be simulated and reconstructed from the XCAT within a reasonable amount of time In comparison with other simulators with geometrically defined organs, the XCAT-based algorithm was found to be only three times slower in generating a projection data set of the same anatomical structures using a single 32 GHz processor To overcome this decrease in speed would, therefore, only require running the projection algorithm in parallel over three processors With the ever decreasing cost of computers and the rise of faster processors and multi-processor systems and clusters, this slowdown is basically inconsequential, especially given the vast improvement the XCAT offers in terms of realism and the ability to generate 3D and 4D data from anatomically diverse patients As such, the authors conclude that the efficient XCAT-based CT simulator developed in this work will have applications in a broad range of CT imaging research

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Realistic CT simulation using the 4D XCAT phantom

The most significant deficiency of most of today's interactive ray tracers is that they are restricted to static walkthroughs. This restriction is due to the static nature of the acceleration structures used. While the best reported frame rates for static geometric models have been achieved using carefully constructed kd-trees, this article shows that bounding volume hierarchies (BVHs) can be used to efficiently ray trace large static models.More importantly, the BVH can be used to ray trace deformable models (sets of triangles whose positions change over time) with little loss of performance. A variety of efficiency techniques are used to achieve this performance, but three algorithmic changes to the typical BVH algorithm are mainly responsible. First, the BVH is built using a variant of the surface area heuristic conventionally used to build kd-trees. Second, the topology of the BVH is not changed over time so that only the bounding volumes need to be refit from frame-to-frame. Third, and most importantly, packets of rays are traced together through the BVH using a novel integrated packet-frustum traversal scheme. This traversal scheme elegantly combines the advantages of both packet traversal and frustum traversal and allows for rapid hierarchy descent for packets that hit bounding volumes as well as rapid exits for packets that miss. A BVH-based ray tracing system using these techniques is shown to achieve performance for deformable models comparable to that previously available only for static models.

Ray tracing deformable scenes using dynamic bounding volume hierarchies

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.

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State of the Art in Ray Tracing Animated Scenes

3D shape completion from partial point clouds is a fundamental problem in computer vision and computer graphics. Recent approaches can be characterized as either data-driven or learning-based. Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations. Learning-based approaches, in contrast, avoid the expensive optimization step and instead directly predict the complete shape from the incomplete observations using deep neural networks. However, full supervision is required which is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. Tackling 3D shape completion of cars on ShapeNet [5] and KITTI [18], we demonstrate that the proposed amortized maximum likelihood approach is able to compete with a fully supervised baseline and a state-of-the-art data-driven approach while being significantly faster. On ModelNet [49], we additionally show that the approach is able to generalize to other object categories as well.

/pdf/learning-3d-shape-completion-from-laser-scan-data-with-weak-2wwt2jzzxa.pdf

Learning 3D Shape Completion from Laser Scan Data with Weak Supervision

Methods, systems, devices and computer program code (software) products operable within a computer graphics system or other computer system enable quasi-Monte Carlo (QMC) light transport simulation by efficient ray tracing.

Quasi-monte carlo light transport simulation by efficient ray tracing

The computational bottleneck in a ray tracer using bounding volume hierarchies is often the ray intersection routine with axis-aligned bounding boxes. We describe a version of this routine that uses IEEE numerical properties to ensure that those tests are both robust and efficient. Sample source code is available online.

An Efficient and Robust Ray-Box Intersection Algorithm

https://www.sowenig.de/wiki/lib/exe/fetch.php?id=raytracing%3Aklassen%3Aaxisalignedboundingbox&cache=cache&media=raytracing:klassen:an_efficient_and_robust_ray_box_intersection_algorithm_box-jgt_.pdf

Steve Barrus

Papers

An Efficient and Robust Ray-Box Intersection Algorithm

An efficient and robust ray-box intersection algorithm

Gauss: A Framework for Verifying Scientific Computing Software