Pattern Recognition Letters
The vector distance transform in two and three dimensions
CVGIP: Graphical Models and Image Processing
On digital distance transforms in three dimensions
Computer Vision and Image Understanding
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Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Adaptively sampled distance fields: a general representation of shape for computer graphics
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A complete distance field representation
Proceedings of the conference on Visualization '01
Fast visualization of plane-like structures in voxel data
Proceedings of the conference on Visualization '02
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Feature Preserving Distance Fields
VV '04 Proceedings of the 2004 IEEE Symposium on Volume Visualization and Graphics
3D Distance Fields: A Survey of Techniques and Applications
IEEE Transactions on Visualization and Computer Graphics
Streaming computation of Delaunay triangulations
ACM SIGGRAPH 2006 Papers
Out-of-core distance transforms
Proceedings of the 2007 ACM symposium on Solid and physical modeling
Separated medial surface extraction from CT data of machine parts
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
O-buffer: a framework for sample-based graphics
IEEE Transactions on Visualization and Computer Graphics
Optimum design of chamfer distance transforms
IEEE Transactions on Image Processing
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We present a Sparse Grid Distance Transform (SGDT), an algorithm for computing and storing large distance fields. Although SGDT is based on a divide-and-conquer algorithm for distance transforms, its data structure is quite simplified. Our observations revealed that distance fields can be recovered from distance fields of sub-block cluster boundaries and the binary information of the cluster through a one-time distance transform. This means that it is sufficient to consider only the cluster boundaries and to represent clusters as binary volumes. As a result, memory usage is less than 0.5% the size of raw files, and it works in-core.