33-Issue 1Regular Issuehttps://diglib.eg.org:443/handle/10.2312/117832023-02-01T11:29:30Z2023-02-01T11:29:30ZPhotons: Evolution of a Course in Data StructuresDuchowski, A. T.https://diglib.eg.org:443/handle/10.1111/v33i1pp294-3042017-07-10T09:20:07Z2014-01-01T00:00:00ZPhotons: Evolution of a Course in Data Structures
Duchowski, A. T.
Holly Rushmeier and Oliver Deussen
This paper presents the evolution of a data structures and algorithms course based on a specific computer graphics problem, namely, photon mapping, as the teaching medium. The paper reports development of the course through several iterations and evaluations, dating back 5 years. The course originated as a problem-based graphics course requiring sophomore students to implement Hoppe et al.'s algorithm for surface reconstruction from unorganized points found in their SIGGRAPH '92 paper of the same title. Although the solution to this problem lends itself well to an exploration of data structures and code modularization, both of which are traditionally taught in early computer science courses, the algorithm's complexity was reflected in students' overwhelmingly negative evaluations. Subsequently, because implementation of the kd-tree was seen as the linchpin data structure, it was again featured in the problem of ray tracing trees consisting of more than 250 000 000 triangles. Eventually, because the tree rendering was thought too specific a problem, the photon mapper was chosen as the semester-long problem considered to be a suitable replacement. This paper details the resultant course description and outline, from its now three semesters of teaching.This paper presents the evolution of a data structures and algorithms course based on a specific computer graphics problem, namely photon mapping, as the teaching medium. The paper reports development of the course through several iterations and evaluations, dating back five years.
2014-01-01T00:00:00ZBoosting Techniques for Physics‐Based Vortex DetectionZhang, L.Deng, Q.Machiraju, R.Rangarajan, A.Thompson, D.Walters, D. K.Shen, H.‐W.https://diglib.eg.org:443/handle/10.1111/v33i1pp282-2932017-07-10T09:20:07Z2014-01-01T00:00:00ZBoosting Techniques for Physics‐Based Vortex Detection
Zhang, L.; Deng, Q.; Machiraju, R.; Rangarajan, A.; Thompson, D.; Walters, D. K.; Shen, H.‐W.
Holly Rushmeier and Oliver Deussen
Robust automated vortex detection algorithms are needed to facilitate the exploration of large‐scale turbulent fluid flow simulations. Unfortunately, robust non‐local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non‐local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms. We motivate the presence of an expert‐in‐the‐loop using empirical results based on machine learning techniques. We employ adaptive boosting to combine a suite of widely used, local vortex detection algorithms, which we term weak classifiers, into a robust compound classifier. Fundamentally, the training phase of the algorithm, in which an expert manually labels small, spatially contiguous regions of the data, incorporates non‐local information into the resulting compound classifier. We demonstrate the efficacy of our approach by applying the compound classifier to two data sets obtained from computational fluid dynamical simulations. Our results demonstrate that the compound classifier has a reduced misclassification rate relative to the component classifiers.Robust automated vortex detection algorithms are needed to facilitate the exploration of large‐scale turbulent fluid flow simulations. Unfortunately, robust non‐local vortex detection algorithms are computationally intractable for large data sets and local algorithms, while computationally tractable, lack robustness. We argue that the deficiencies inherent to the local definitions occur because of two fundamental issues: the lack of a rigorous definition of a vortex and the fact that a vortex is an intrinsically non‐local phenomenon. As a first step towards addressing this problem, we demonstrate the use of machine learning techniques to enhance the robustness of local vortex detection algorithms.
2014-01-01T00:00:00ZOn Near Optimal Lattice Quantization of Multi‐Dimensional Data PointsFinckh, M.Dammertz, H.Lensch, H. P. A.https://diglib.eg.org:443/handle/10.1111/v33i1pp271-2812017-07-10T09:20:07Z2014-01-01T00:00:00ZOn Near Optimal Lattice Quantization of Multi‐Dimensional Data Points
Finckh, M.; Dammertz, H.; Lensch, H. P. A.
Holly Rushmeier and Oliver Deussen
One of the most elementary application of a lattice is the quantization of real‐valued s‐dimensional vectors into finite bit precision to make them representable by a digital computer. Most often, the simple s‐dimensional regular grid is used for this task where each component of the vector is quantized individually. However, it is known that other lattices perform better regarding the average quantization error. A rank‐1 lattices is a special type of lattice, where the lattice points can be described by a single s‐dimensional generator vector. Further, the number of points inside the unit cube [0, 1)s is arbitrary and can be directly enumerated by a single one‐dimensional integer value. By choosing a suitable generator vector the minimum distance between the lattice points can be maximized which, as we show, leads to a nearly optimal mean quantization error. We present methods for finding parameters for s‐dimensional maximized minimum distance rank‐1 lattices and further show their practical use in computer graphics applications.One of the most elementary application of a lattice is the quantization of real valued s‐dimensional vectors into finite bit precision to make them representable by a digital computer. Most often, the simple s‐dimensional regular grid is used for this task where each component of the vector is quantized individually. However, it is known that other lattices perform better regarding the average quantization error. A rank‐1 lattices is a special type of lattice, where the lattice points can be described by a single s‐dimensional generator vector.
2014-01-01T00:00:00ZInteractive Simulation of Rigid Body Dynamics in Computer GraphicsBender, JanErleben, KennyTrinkle, Jeffhttps://diglib.eg.org:443/handle/10.1111/v33i1pp246-2702017-07-10T09:20:07Z2014-01-01T00:00:00ZInteractive Simulation of Rigid Body Dynamics in Computer Graphics
Bender, Jan; Erleben, Kenny; Trinkle, Jeff
Holly Rushmeier and Oliver Deussen
Interactive rigid body simulation is an important part of many modern computer tools, which no authoring tool nor game engine can do without. Such high-performance computer tools open up new possibilities for changing how designers, engineers, modelers and animators work with their design problems. This paper is a self contained state-of-the-art report on the physics, the models, the numerical methods and the algorithms used in interactive rigid body simulation all of which have evolved and matured over the past 20 years. Furthermore, the paper communicates the mathematical and theoretical details in a pedagogical manner. This paper is not only a stake in the sand on what has been done, it also seeks to give the reader deeper insights to help guide their future research.
2014-01-01T00:00:00Z