We present a novel way to efficiently compute Riemannian geodesic distance over a two- or three-dimensional domain. It is based on a previously presented method for computation of geodesic distances on surface meshes. Our method is adapted for rectangular grids, equipped with a variable anisotropic metric tensor. Processing and visualization of such tensor fields is common in certain applications, for instance structure tensor fields in image analysis and diffusion tensor fields in medical imaging. The included benchmark study shows that our method provides significantly better results in anisotropic regions in 2-D and 3-D and is faster than current stat-of-the-art solvers in 2-D grids. Additionally, our method is straightforward to code; the test implementation is less than 150 lines of C++ code. The paper is an extension of a previously presented conference paper and includes new sections on 3-D grids in particular.

The work in this thesis focuses on efficient implementations of level-set methods and geodesic distance functions. The level-set method is a grid based design that inherits many favorable traits from implicit geometry. It is connected to distance functions through its special way of representing geometry: in ìo each point in space stores the closest distance to the surface. To differentiate between the inside and outside of a closed object a signed distance is used. In the discrete form the representation keeps a box around the surface that stores regularly positioned samples of the distance function – i.e. a grid. These samples implicitly encode the surface as the zeroth level-set of the signed distance function, hence the name level-set methods. With this representation of geometry follows a toolbox of operations based on partial differential equations (PDE). The solution to these PDES allows for arbitrary motion and deformation of the surface.

This thesis focuses on two topics: 1) grid storage for level-set methods, and 2) geodesic distance functions and parameterization. These topics are covered in a series of in-depth articles.

Today, level-set methods are becoming widespread in both academia and industry. Data structures and highly accurate methods and numerical schemes are available that allow for efficient handling of topological changes of dynamic curves and surfaces. For some applications, such as the capturing of the air/water interface in free surface fluid simulations, it’s is the only realistic choice. In other areas level-set methods are emerging as a competitive candidate to triangle meshes and other explicit representations.

In particular this work introduces efficient level-set data-structures that allow for extremely detailed simulations and representations. It also presents a parameterization method based on geodesic distance that produces a unique coordinate system, the Riemannian normal coordinates (RNC). Amongst other interesting applications this parameterization can be used for decal compositing, and the translation of vector space algorithms to surfaces. The approximation of the RNC involves one or more distance functions. In this thesis, a method originally presented for triangle meshes is adopted. It is then and extended to compute accurate geodesic distance in anisotropic domains in two and three dimensions. The extension to higher dimensions is also outlined.

To motivate this work several applications based on these novel methods and data structures are presented showing rapid ray-tracing, shape morphing, segmentation, geodesic interpolation, texture mapping, and more.

We compare the most frequently used algorithms for computing distance transforms in terms of speed, scalability and accuracy. The algorithms we consider are: partial differential equation based distancing methods of different finite difference accuracy, fast marching methods and fast sweeping methods. Our results show clearly that all the first order algorithms perform similarly in terms of accuracy and that the only major difference is efficiency. In parallel with the revision of the preprint we find that the newly proposed O(N) quantized fast marching algorithm in practice does not scale linearly.

This article presents a generic framework for the representation and deformation of level set surfaces at extreme resolutions. The framework is composed of two modules that each utilize optimized and application specific algorithms: 1) A fast out-of-core data management scheme that allows for resolutions of the deforming geometry limited only by the available disk space as opposed to memory, and 2) compact and fast compression strategies that reduce both offline storage requirements and online memory footprints during simulation. Out-of-core and compression techniques have been applied to a wide range of computer graphics problems in recent years, but this article is the first to apply it in the context of level set and fluid simulations. Our framework is generic and flexible in the sense that the two modules can transparently be integrated, separately or in any combination, into existing level set and fluid simulation software based on recently proposed narrow band data structures like the DT-Grid of Nielsen and Museth [2006] and the H-RLE of Houston et al. [2006]. The framework can be applied to narrow band signed distances, fluid velocities, scalar fields, particle properties as well as standard graphics attributes like colors, texture coordinates, normals, displacements etc. In fact, our framework is applicable to a large body of computer graphics problems that involve sequential or random access to very large co-dimension one (level set) and zero (e.g. fluid) data sets. We demonstrate this with several applications, including fluid simulations interacting with large boundaries (? 15003), surface deformations (? 20483), the solution of partial differential equations on large surfaces (˜40963) and mesh-to-level set scan conversions of resolutions up to ? 350003 (7 billion voxels in the narrow band). Our out-of-core framework is shown to be several times faster than current state-of-the-art level set data structures relying on OS paging. In particular we show sustained throughput (grid points/sec) for gigabyte sized level sets as high as 65% of state-of-the-art throughput for in-core simulations. We also demonstrate that our compression techniques out-perform state-of-the-art compression algorithms for narrow bands. © 2007 ACM.

We present work in progress on concurrent ray tracing with distributed computers using ``off-the-shelf'' open source software. While there exists numerous open source ray tracers, very few offer support for state-of-the-art concurrent computing. However, it is a well known fact that ray tracing is computationally intensive and yet prevails as the preferred algorithm for photorealistic rendering. Thus, the current work is driven by a desire for a simple programming strategy (or recipe) that allows pre-existing ray tracing code to be parallelized on a heterogenous cluster of available office computers - strictly using open source components. Simplicity, stability, efficiency and modularity are the driving forces for this engineering project, and as such we do not claim any novel research contributions. However, we stress that this project grew out of a real-world need for a render cluster in our research group, and consequently our solutions have a significant practical value. In fact some of our results show a close to optimal speedup when considering the relative performances of each node. In this systems paper we aim at sharing these solutions and experiences with other members of the graphics community.

This article introduces the Hierarchical Run-Length Encoded (H-RLE) Level Set data structure. This novel data structure combines the best features of the DT-Grid ( of Nielsen and Museth [ 2004]) and the RLE Sparse Level Set ( of Houston et al. [ 2004]) to provide both optimal efficiency and extreme versatility. In brief, the H- RLE level set employs an RLE in a dimensionally recursive fashion. The RLE scheme allows the compact storage of sequential nonnarrowband regions while the dimensionally recursive encoding along each axis efficiently compacts nonnarrowband planes and volumes. Consequently, this new structure can store and process level sets with effective voxel resolutions exceeding 5000 x 3000 x 3000 ( 45 billion voxels) on commodity PCs with only 1 GB of memory. This article, besides introducing the H- RLE level set data structure and its efficient core algorithms, also describes numerous applications that have benefited from our use of this structure: our unified implicit object representation, efficient and robust mesh to level set conversion, rapid ray tracing, level set metamorphosis, collision detection, and fully sparse fluid simulation ( including RLE vector and matrix representations.) Our comparisons of the popular octree level set and Peng level set structures to the H- RLE level set indicate that the latter is superior in both narrowband sequential access speed and overall memory usage.

We present a robust method for 3D reconstruction of closed surfaces from sparsely sampled parallel contours. A solution to this problem is especially important for medical segmentation, where manual contouring of 2D imaging scans is still extensively used. Our proposed method is based on a morphing process applied to neighboring contours that sweeps out a 3D surface. Our method is guaranteed to produce closed surfaces that exactly pass through the input contours, regardless of the topology of the reconstruction.

Our general approach consecutively morphs between sets of input contours using an Eulerian formulation (i.e. fixed grid) augmented with Lagrangian particles (i.e. interface tracking). This is numerically accomplished by propagating the input contours as 2D level sets with carefully constructed continuous speed functions. Specifically this involves particle advection to estimate distances between the contours, monotonicity constrained spline interpolation to compute continuous speed functions without overshooting, and stateof- the-art numerical techniques for solving the level set equations. We demonstrate the robustness of our method on a variety of medical, topographic and synthetic data sets.

Imagine an ant walking around on the curved surface of a plant, a radio amateur planning to broadcast to a distant location across the globe or a pilot taking o from an airport - all of them are helped by egocentric maps of the world around them that shows directions and distances to various remote places. It is not surprising that this idea has already been used in cartography, where it is known as Azimuthal Equidistant Projection (AEP). If Earth is approximated by a sphere, distances and directions between two places are computed from arcs along great circles. In physics and mathematics, the same idea is known as Riemannian Normal Coordinates (RNC). It has been given a precise and general denition for surfaces (2-D), curved spaces (3-D) and generalized to smooth manifolds (N-D). RNC are the Cartesian coordinates of vectors that index points on the surface (or manifold) through the so called exponential map, which is a well known concept in dierential geometry. They are easily computed for a particular point if the inverse of the exponential map, the logarithm map, is known. Recently, RNC and similar coordinate systems have been used in computer graphics, visualization and related areas of research. In Fig. 1 for instance, RNC are used to produce a texture on the Stanford bunny through decal compositing. Given the growing use of RNC, which is further elaborated on in the next section, it is meaningful to develop accurate and reproducible techniques to compute this parameterization. In this paper, we describe a technique to compute RNC for surfaces represented by triangular meshes, which is the predominant representation of surfaces in computer graphics. The method that we propose has similarities to the Logmap framework, which has previously been developed for dimension reduction of unorganized point clouds in high-dimensional spaces, a.k.a. manifold learning. For this reason we sometimes refer to it as "Logmap for triangular meshes" or simply Logmap.

We present a novel way to efficiently compute anisotropic distances over a tessellated domain in two dimensions. The method is based on an integral formulation of distance and entails solving a dynamic programming problem. We also present an intuitive geometric construction that is used to characterize dierent types of boundary conditions and show how they aect the resulting distance function in our and competing work.

The included benchmark study shows that our method provides signicantly better results in anisotropic regions and is faster than a current stat-of-the-art solver. Additionally, our method is straightforward to code; the test implementation is less than 150 lines of C++ code.