Algorithms for Interactive Editing of Level Set Models

Museth, Ken; Breen, David E.; Whitaker, Ross T.; Mauch, Sean; Johnson, David E.

doi:10.1111/j.1467-8659.2005.00904.x

Cited by 27 publications

(10 citation statements)

References 74 publications

(90 reference statements)

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“…Boolean subtraction is trivial in such representations. We specifically use narrow-band level sets [15], which represent the surface as the zero iso-contour of an approximate distance field. We then use marching cubes [9] to produce the final mesh for printing.…”

Section: Mesh Modificationmentioning

confidence: 99%

A series of tubes

Savage

Schmidt

Grossman

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

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Figure 1. A collection of interactive objects designed using our tool. All have electronic sensing or actuation components routed through their interior. Left: a radio, haptic feedback rabbit, maze, and presence-aware pen holder. Center: touch-sensitive toys. Right: custom neon signs. ABSTRACT3D printers offer extraordinary flexibility for prototyping the shape and mechanical function of objects. We investigate how 3D models can be modified to facilitate the creation of interactive objects offering dynamic input and output. We introduce a general technique to support rapidly prototyping interactivity by removing interior material from 3D models to form internal pipes. We describe the design space of pipes for interaction design, where variables include openings, path constraints, topologies, and inserted media. We then present PipeDream, a tool for routing internal pipes through 3D models, integrated within a 3D modeling program. We use two distinct routing algorithms. The first has users define pipes' terminals and uses path routing and physics-based simulation to minimize pipe bending energy, allowing easy insertion of media post-print. The second lets users supply a desired internal shape to which it fits a pipe route: for this we describe a graph-routing algorithm. We present prototypes created using our tool showing its flexibility and potential.

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Section: Mesh Modificationmentioning

confidence: 99%

A series of tubes

Savage

Schmidt

Grossman

et al. 2014

Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

show abstract

“…The band reinitialisation is performed each iteration using a simple ±1 scheme. This reduction in accuracy is tolerable for most applications and its implementation has allowed the level set family of methods to achieve real-time performance in complex 3D applications [14], [12].…”

Section: Introductionmentioning

confidence: 99%

3D level set image segmentation refined by intelligent agent swarm

Feltell

2010

IEEE Congress on Evolutionary Computation

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The level set method of surface representation and deformation has found many applications in image processing, especially with regard to segmentation. Naive numerical solutions have long since given way to much more efficient narrow band methods, where updates to the scalar field are performed only within a number of layers either side of the surface. This paper presents our implementation of automated segmentation via the sparse field narrow band approach. We use k-means clustering of regularly sampled points to provide initialisation of seed locations and mean voxel values, removing the need for manual seed and parameter selection. We demonstrate this method by segmenting grey matter and white matter from a simulated T1 MRI scan.Anisotropy can easily cause this and other segmentation methods to misclassify voxels, however. Often it is clear in a 3D view of the resulting surface which regions require refinement. With available extensions to the sparse field and rendering algorithms we can now efficiently perform purely localised modifications and re-rendering of the surface. These extensions, plus properties inherent in the level set method, allow us to easily situate swarms of agents on the surface, intelligently inhabiting and modifying the zero-level set. By a simple weighting of movement direction, we can direct the swarm to any user-specified point on the surface in order to affect repairs and refinement.978-1-4244-8126-2/10/$26.00 ©2010 IEEE

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“…Often, this transform is represented as a distance volume, which is a discrete sampling of point positions with their minimum distances to a model stored in a volumetric array. Distance volumes have the advantage of being extremely fast, since finding a distance is just looking up the appropriate value in a table [Museth et al 2005]. They have the disadvantage of requiring large storage and of being a fixed, usually crude, resolution.…”

Section: Distance Transformmentioning

confidence: 99%

A higher dimensional formulation for robust and interactive distance queries

Seong

Johnson

Cohen

2006

Proceedings of the 2006 ACM Symposium on Solid and Physical Modeling

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We present an efficient and robust algorithm for computing the minimum distance between a point and freeform curve or surface by lifting the problem into a higher dimension. This higher dimensional formulation solves for all query points in the domain simultaneously, therefore providing opportunities to speed computation by applying coherency techniques. In this framework, minimum distance between a point and planar curve is solved using a single polynomial equation in three variables (two variables for a position of the point and one for the curve). This formulation yields twomanifold surfaces as a zero-set in a 3D parameter space. Given a particular query point, the solution space's remaining degrees-offreedom are fixed and we can numerically compute the minimum distance in a very efficient way. We further recast the problem of analyzing the topological structure of the solution space to that of solving two polynomial equations in three variables. This topological information provides an elegant way to efficiently find a global minimum distance solution for spatially coherent queries. Additionally, we extend this approach to a 3D case. We formulate the problem for the surface case using two polynomial equations in five variables. The effectiveness of our approach is demonstrated with several experimental results.

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Algorithms for Interactive Editing of Level Set Models

Cited by 27 publications

References 74 publications

A series of tubes

A series of tubes

3D level set image segmentation refined by intelligent agent swarm

A higher dimensional formulation for robust and interactive distance queries

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