Turk, Greg

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Now showing 1 - 10 of 14
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    Variational Implicit Surfaces
    (Georgia Institute of Technology, 1999) Turk, Greg ; O'Brien, James F.
    We introduce a new method of creating smooth implicit surfaces of arbitrary manifold topology. These surfaces are described by specifying locations in 3D through which the surface should pass, and also identifying locations that are interior or exterior to the surface. A 3D implicit function is created from these constraints using a variational scattered data interpolation approach. We call the iso-surface of this function a \emph{variational implicit surface}. Like other implicit surface descriptions, these surfaces can be used for CSG and interference detection, may be interactively manipulated, are readily approximated by polygonal tilings, and are easy to ray trace. A key strength is that variational implicit surfaces allow the direct specification of both the location of points on the surface and surface normals. These are two important manipulation techniques that are difficult to achieve using other implicit surface representations such as sums of spherical or ellipsoidal Gaussian functions (``blobbies''). We show that these properties make variational implicit surfaces particularly attractive for interactive sculpting using the particle sampling technique introduced by Witkin and Heckbert in~\cite{Witkin}. Our formulation also yields a simple method for converting a polygonal model to a smooth implicit model.
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    Image-Driven Simplification
    (Georgia Institute of Technology, 1999) Lindstrom, Peter ; Turk, Greg
    We introduce the notion of image-driven simplification, a framework that uses images to decide which portions of a model to simplify. This is a departure from approaches that make polygonal simplification decisions based on geometry. As with many methods, we use the edge collapse operator to make incremental changes to a model. Unique to our approach, however, is the use of comparisons between images of the original model against those of a simplified model to determine the cost of an edge collapse. We use common graphics rendering hardware to accelerate the creation of the required images. As expected, this method produces models that are close to the original model according to image differences. Perhaps more surprising, however, is that the method yields models that have high geometric fidelity as well. Our approach also solves the quandary of how to weight the geometric distance versus appearance properties such as normals, color and texture. All of these tradeoffs are balanced by the image metric. Benefits of this approach include high fidelity silhouettes, extreme simplification of hidden portions of a model, attention to shading interpolation effects, and simplification that is sensitive to the content of a texture. In order to better preserve the appearance of textured models, we introduce a novel technique for assigning texture coordinates to the new vertices of the mesh. This method is based on a geometric heuristic that can be integrated with any edge collapse algorithm to produce high quality textured surfaces.
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    Vector Field Design on Surfaces
    (Georgia Institute of Technology, 2004) Zhang, Eugene ; Mischaikow, Konstantin Michael ; Turk, Greg
    Vector field design on surfaces is necessary for many graphics applications: example-based texture synthesis, non-photorealistic rendering, and fluid simulation. A vector field design system should allow a user to create a large variety of complex vector fields with relatively little effort. In this paper, we present a vector field design system for surfaces that allows the user to control the number of singularities in the vector field and their placement. Our system combines basis vector fields to make an initial vector field that meets the user's specifications. The initial vector field often contains unwanted singularities. Such singularities cannot always be eliminated, due to the Poincar'e-Hopf index theorem. To reduce the effect caused by these singularities, our system allows a user to move a singularity to a more favorable location or to cancel a pair of singularities. These operations provide topological guarantees for the vector field in that they only affect the user-specified singularities. Other editing operations are also provided so that the user may change the topological and geometric characteristics of the vector field. We demonstrate our vector field design system for several applications: example-based texture synthesis, painterly rendering of images, and pencil sketch illustrations of smooth surfaces.
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    Feature-Based Surface Parameterization and Texture Mapping
    (Georgia Institute of Technology, 2003) Zhang, Eugene ; Mischaikow, Konstantin Michael ; Turk, Greg
    Surface parameterization is necessary for many graphics tasks: texture-preserving simplification, remeshing, surface painting, and pre-computation of solid textures. The stretch caused by a given parameterization determines the sampling rate on the surface. In this paper, we propose an automatic parameterization method that segments a surface into patches that are then flattened with little stretch. We observe that many objects consist of regions of relative simple shapes, each of which has a natural parameterization. Therefore, we propose a three-stage feature based patch creation method for manifold mesh surfaces. The first two stages, genus reduction and feature identification, are performed with the help of distance-based Morse functions. In the last stage, we create one or two patches for each feature region based on a covariance matrix of the feature's surface points. To reduce the stretch during patch unfolding, we notice that the stretch is a 2x2 tensor which in ideal situations is the identity. Therefore, we propose to use the Green-Lagrange tensor to measure and to guide the optimization process. Furthermore, we allow the boundary vertices of a patch to be optimized by adding scaffold triangles. We demonstrate our feature identification and patch unfolding methods for several textured models. Finally, to evaluate the quality of a given parameterization, we propose an image-based error measure that takes into account stretch, seams, smoothness, packing efficiency and visibility.
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    Image-Driven Mesh Optimization
    (Georgia Institute of Technology, 2000) Lindstrom, Peter ; Turk, Greg
    We describe a method of improving the appearance of a low vertex count mesh in a manner that is guided by rendered images of the original, detailed mesh. This approach is motivated by the fact that greedy simplification methods often yield meshes that are poorer than what can be represented with a given number of vertices. Our approach relies on edge swaps and vertex teleports to alter the mesh connectivity, and uses the downhill simplex method to simultaneously improve vertex positions and surface attributes. Note that this is not a simplification method--the vertex count remains the same throughout the optimization. At all stages of the optimization the changes are guided by a metric that measures the differences between rendered versions of the original model and the low vertex count mesh. This method creates meshes that are geometrically faithful to the original model. Moreover, the method takes into account more subtle aspects of a model such as surface shading or whether cracks are visible between two interpenetrating parts of the model.
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    Fast and Memory Efficient Polygonal Simplification
    (Georgia Institute of Technology, 1998) Lindstrom, Peter ; Turk, Greg
    Conventional wisdom says that in order to produce high-quality simplified polygonal models, one must retain and use information about the original model during the simplification process. We demonstrate that excellent simplified models can be produced without the need to compare against information from the original geometry while performing local changes to the model. We use edge collapses to perform simplification, as do a number of other methods. We select the position of the new vertex so that the original volume of the model is maintained and we minimize the per-triangle change in volume of the tetrahedra swept out by those triangles that are moved. We also maintain surface area near boundaries and minimize the per-triangle area changes. Calculating the edge collapse priorities and the positions of the new vertices requires only the face connectivity and the the vertex locations in the intermediate model. This approach is memory efficient, allowing the simplification of very large polygonal models, and it is also fast. Moreover, simplified models created using this technique compare favorably to a number of other published simplification methods in terms of mean geometric error.
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    Reconstructing Surfaces by Volumetric Regularization
    (Georgia Institute of Technology, 2000) Dinh, Huong Quynh ; Turk, Greg ; Slabaugh, Gregory G.
    We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, and non-uniform range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce three dimensional point sets that are imprecise and non-uniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data cannot be used on stereo range images and space carved volumes. Our method constructs a three dimensional implicit surface, formulated as a summation of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data; (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data; and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.
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    Vessel Segmentation Using a Shape Driven Flow
    (Georgia Institute of Technology, 2004-09) Nain, Delphine ; Yezzi, Anthony ; Turk, Greg ; Georgia Institute of Technology. School of Electrical and Computer Engineering
    We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branches. Using image intensity alone to deform a model for the task of segmentation often results in leakages at areas where the image information is ambiguous. To address this problem, we combine image statistics and shape information to derive a region-based active contour that segments tubular structures and penalizes leakages. We present results on synthetic and real 2D and 3D datasets.
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    Creating Smooth Implicit Surfaces from Polygonal Meshes
    (Georgia Institute of Technology, 1999) Yngve, Gary ; Turk, Greg
    Implicit surfaces have long been used for a myriad of tasks in computer graphics, including modeling soft or organic objects, morphing, and constructive solid geometry. Although operating on implicit surfaces is usually straight-forward, creating them is not --- interactive techniques are impractical for complex models, and automatic techniques have been largely unexplored. We introduce a practical method for creating implicit surfaces from polygonal models that produces high-quality results for complex models. Whereas much previous work has been done with primitives such as ``blobbies,'' we use surfaces based on a variational interpolation technique (the 3D generalization of thin-plate interpolation). Given a polygonal mesh, we convert the data to a volumetric representation and use this as a guide to create the implicit surface iteratively. Carefully chosen metrics evaluate each intermediate surface and control further refinement. We have applied this method successfully to a variety of polygonal meshes.
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    Data-Driven Haptic Perception for Robot-Assisted Dressing
    (Georgia Institute of Technology, 2016-08) Kapusta, Ariel ; Yu, Wenhao ; Bhattacharjee, Tapomayukh ; Liu, C. Karen ; Turk, Greg ; Kemp, Charles C. ; Georgia Institute of Technology. Rehabilitation Engineering Research Center on Technologies to Support Successful Aging With Disability ; Georgia Institute of Technology. Healthcare Robotics Lab ; Georgia Institute of Technology. School of Interactive Computing
    Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data-driven haptic perception can be used to infer relationships between clothing and the human body during robot-assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person’s forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot’s end effector classified these outcomes with high accuracy. The HMMs’ performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold), and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.