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ABSTRACT: Typically 3-D MR and CT scans have a relatively high resolution in the scanning X-Y plane, but much lower resolution in the axial Z direction. This non-uniform sampling of an object can miss small or thin structures. One way to address this problem is to scan the same object from multiple directions. In this paper we describe a method for deforming a level set model using velocity information derived from multiple volume datasets with non-uniform resolution in order to produce a single high-resolution 3D model. The method locally approximates the values of the multiple datasets by fitting a distance-weighted polynomial using moving least-squares. The proposed method has several advantageous properties: its computational cost is proportional to the object surface area, it is stable with respect to noise, imperfect registrations and abrupt changes in the data, it provides gain-correction, and it employs a distance-based weighting to ensures that the contributions from each scan are properly merged into the final result. We have demonstrated the effectiveness of our approach on four multi-scan datasets, a Griffin laser scan reconstruction, a CT scan of a teapot and MR scans of a mouse embryo and a zucchini.
Visualization, 2002. VIS 2002. IEEE; 12/2002
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ABSTRACT: We present a new approach to 3D shape metamorphosis. We express
the interpolation of two shapes as a process where one shape deforms to
maximize its similarity with another shape. The process incrementally
optimizes an objective function while deforming an implicit surface
model. We represent the deformable surface as a level set (iso-surface)
of a densely sampled scalar function of three dimensions. Such level-set
models have been shown to mimic conventional parametric deformable
surface models by encoding surface movements as changes in the grayscale
values of a volume data set. Thus, a well-founded mathematical structure
leads to a set of procedures that describes how voxel values can be
manipulated to create deformations that are represented as a sequence of
volumes. The result is a 3D morphing method that offers several
advantages over previous methods, including minimal need for user input,
no model parameterization, flexible topology, and subvoxel
accuracy
IEEE Transactions on Visualization and Computer Graphics 05/2001; · 2.21 Impact Factor
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ABSTRACT: A distance volume is a volume dataset where the value stored at each voxel is the shortest distance to the surface of the object being represented by the volume. Distance volumes are a useful representation in a number of computer graphics applications. We present a technique for generating a distance volume with sub-voxel accuracy from one type of geometric model, a constructive solid geometry (CSG) model consisting of superellipsoid primitives. The distance volume is generated in a two step process. The first step calculates the shortest distance to the CSG model at a set of points within a narrow band around the evaluated surface. Additionally, a second set of points, labeled the zero set, which lies on the CSG model's surface are computed. A point in the zero set is associated with each point in the narrow band. Once the narrow band and zero set are calculated, a fast marching method is employed to propagate the shortest distance and closest point information out to the remaining voxels in the volume. Our technique has been used to scan convert a number of CSG models, producing distance volumes which have been utilized in a variety of computer graphics applications, e.g. CSG surface evaluation, offset surface generation, and 3D model morphing.
Volume Visualization, 1998. IEEE Symposium on; 11/1998
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ABSTRACT: Augmented reality entails the use of models and their associated
renderings to supplement information in a real scene. In order for this
information to be relevant or meaningful, the models must be positioned
and displayed in such a way that they blend into the real world in terms
of alignments, perspectives, illuminations, etc. For practical reasons
the information necessary to obtain this realistic blending cannot be
known a priori, and cannot be hard wired into a system. Instead a number
of calibration procedures are necessary so that the location and
parameters of each of the system components are known. We identify the
calibration steps necessary to build a computer model of the real world
and then, using the monitor based augmented reality system developed at
ECRC (GRASP) as an example, we describe each of the calibration
processes. These processes determine the internal parameters of our
imaging devices (scan converter, frame grabber, and video camera), as
well as the geometric transformations that relate all of the physical
objects of the system to a known world coordinate system
IEEE Transactions on Visualization and Computer Graphics 10/1995; · 2.21 Impact Factor
