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
Three-dimensional distance field metamorphosis
ACM Transactions on Graphics (TOG)
Fast computation of generalized Voronoi diagrams using graphics hardware
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applications of the region growing Euclidean distance transform: anisotropy and skeletons
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Nonconvex rigid bodies with stacking
ACM SIGGRAPH 2003 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Distance Fields: A Survey of Techniques and Applications
IEEE Transactions on Visualization and Computer Graphics
Separated medial surface extraction from CT data of machine parts
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
Optimum design of chamfer distance transforms
IEEE Transactions on Image Processing
Volume graphics modeling of ice thawing
VG'01 Proceedings of the 2001 Eurographics conference on Volume Graphics
Sparse grid distance transforms
Graphical Models
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This paper presents a method for computing distance fields from large volumetric models. Conventional methods have strict limits in terms of the amount of memory space available, as all volumetric models must be allocated to the random access memory (RAM) to compute distance fields. We resolve this problem through an out-of-core strategy. Our algorithm starts by decomposing volumetric models into small regions known as clusters, and distance fields are then computed by Local Distance Transform (LDT) and Inter-Cluster Propagation (ICP). LDT computes the distance transform for each cluster, and since it is independent, other clusters can also be saved to the storage medium. ICP propagates the distance at the boundary of the cluster to neighboring clusters to remove inconsistency in distance fields. In addition, we propose an efficient ordering algorithm based on the propagated distance to reduce LDT and ICP. This paper also demonstrates the results of distance transform from volumetric models with over a billion cells.