&lt;title&gt;Acceleration of ray-casting using 3-D distance transforms&lt;/title&gt;

Zuiderveld, Karel J.; Koning, Anton H. J.; Viergever, Max A.

doi:10.1117/12.131088

Cited by 95 publications

(39 citation statements)

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“…In chamfer distance transforms (CDTs) [97], [96], [141], [18], [2], the distance template (Fig. 4) is centered over each voxel, and the distance at the central voxel is computed as the minimum of all of its neighbors' distances with the appropriate component added.…”

Section: Chamfer Distance Transformmentioning

confidence: 99%

3D distance fields: a survey of techniques and applications

Jones

Bærentzen

Šrámek³

2006

IEEE Trans. Visual. Comput. Graphics

338

195

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Abstract-A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?

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Section: Chamfer Distance Transformmentioning

confidence: 99%

3D distance fields: a survey of techniques and applications

Jones

Bærentzen

Šrámek³

2006

IEEE Trans. Visual. Comput. Graphics

338

195

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“…These data structures are built during a preprocessing step from a classified volume: a volume to which an opacity transfer function has been applied. Such spatial data structures include octrees and pyramids [13] [12] [8] [3], k-d trees [18] and distance transforms [23]. Although this type of optimization is data-dependent, researchers have reported that in typical classified volumes 70-95% of the voxels are transparent [12] [18].…”

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confidence: 99%

Fast volume rendering using a shear-warp factorization of the viewing transformation

Lacroute

Levoy

1994

Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH '94

730

453

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Several existing volume rendering algorithms operate by factoring the viewing transformation into a 3D shear parallel to the data slices, a projection to form an intermediate but distorted image, and a 2D warp to form an undistorted final image. We extend this class of algorithms in three ways. First, we describe a new object-order rendering algorithm based on the factorization that is significantly faster than published algorithms with minimal loss of image quality. Shear-warp factorizations have the property that rows of voxels in the volume are aligned with rows of pixels in the intermediate image. We use this fact to construct a scanline-based algorithm that traverses the volume and the intermediate image in synchrony, taking advantage of the spatial coherence present in both. We use spatial data structures based on run-length encoding for both the volume and the intermediate image. Our implementation running on an SGI Indigo workstation renders a 2563 voxel medical data set in one second. Our second extension is a shearwarp factorization for perspective viewing transformations, and we show how our rendering algorithm can support this extension. Third, we introduce a data structure for encoding spatial coherence in unclassified volumes (i.e. scalar fields with no precomputed opacity). When combined with our shear-warp rendering algorithm this data structure allows us to classify and render a 2563 voxel volume in three seconds. The method extends to support mixed volumes and geometry and is parallelizable.

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“…Since our optimal brick size is 32 3 ; this would mean that we would need 32 3 different look-up tables to address the neighboring samples. In the re-sampling case, 7 neighbors need to be addressed; accordingly the size of the look-up tables would be 32 3Ã 7 Ã 4 Bytes ¼ 896 KB (4 Bytes per offset).…”

Section: Article In Pressmentioning

confidence: 99%

“…Two of them are hardware based; the first one utilizes commodity graphics-cards; the second one utilizes special purpose hardware, e.g. VolumePro and Vizard; the third is CPU based [1][2][3][4][5][6]. Purely hardware based solutions provide real-time performance and high quality; however they are limited in their functionalities: Basic visualization systems are supported by hardware volume rendering solutions; consequently they are the mostly applied approach in practice.…”

Section: Introductionmentioning

confidence: 99%