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Hierarchical Volume Rendering

Hierarchical Volume Rendering. …. 8x8x8 4x4x4 2x2x2 …. Big Data Problem …. One way to handle the big data problem is to use hierarchical data structures (hierarchical volumes). Less amount of data are required. Issues.

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Hierarchical Volume Rendering

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  1. Hierarchical Volume Rendering

  2. … 8x8x8 4x4x4 2x2x2 … Big Data Problem … • One way to handle the big data problem is to use hierarchical data structures (hierarchical volumes) Less amount of data are required

  3. Issues • Creation of hierarchical data structures • Utilization of hierarchical data structures

  4. Entire volume Half in each dim … Hierarchical Volume • A hierarchical volume can be constructed using the octree

  5. Octrees • An octree can be full or non-full • Full octree – each parent node has exact 8 children • Condition: all the dimensions are the same power of 2 • A full octree example: 16 x 16 x 16 • Level 1: (8) 8 x 8 x 8; level 2: (8) 4 x 4 x 4 Level 3: (8) 2 x 2 x 2; level 4: (8) 1 x1 x 1

  6. Octrees (2) • For a volume with resolution s in each dimension, the total number of tree nodes is: 8^0 + 8 ^ 1 + … + 8 ^ (log S –1) = (s ^ 3 –1 )/7 • Node to data ratio: (S^3 –1 ) / 7*S^3 = 0.1428 this is the optimal ratio • If a volume can not be represented by a full octree, the node to data ratio will increase -> overhead increases 2

  7. Octrees (3) • Other benefit of full octree - Can be stored in a linear array without using pointers • Each parent has exact 8 children – their positions in an array are totally predictable • For a parent stored at T[k], then its eight children are stored at T[8k+1], T[8k+2], …, T[8k+8]. • All the nodes at the same level are stored in a contiguous memory space

  8. Octrees (4) • Power of 2 volume is not a norm • Full-octree thus is not always possible • A non-full octree example: 16 x 8 x 4 • Level 1: (8) 8 x 4 x 2; level 2: (4) 4 x 2 x 1 Level 3: (4) 2 x 1 x 1; level 4: (2) 1 x1 x 1 • Total number of nodes: 1 + 8 +8* 4 + 8*4*4 = 161 • The node to data ratio = 161 / 512 = 0.314 > optimal ( 0.1428) • Also , we need to store the pointers from parents to children

  9. Octrees (5) • In general, there are two ways to construct an octree for a volume • Top-down: 1 -> 8 -> 8x8 -> 8x8x8 -> 8x8x8x4 -> …. -> n (n = total number of data) • Buttom-up: n -> n / 8 -> n / 8x8 -> n/8x8x8 -> n/8x8x8x4 -> … -> 1 • Which one has a better node/data ratio?

  10. Branch-on-Need (BON) Octrees • A top-down implementation of bottom-up octrees • Overlay the volume with a conceptual all power of 2 volume • Subdivide the volume based on the concpetual volume

  11. BONO (BON-Octrees) 5x5 8x8

  12. (1) (4) (9) (2) (2) (1) (4)(4)(4)(4) (2) (2) BONO Construction 10 11 00 01

  13. BONO v.s top-down • Put more 8-way branches toward to the leaf nodes • In general, BONO requires a smaller number of tree nodes

  14. Octrees and visualization • Isosurface Extraction • Each BONO node stores the min/max values of the corresponding subvolume • The empty node can be skipped rapidly

  15. Octrees for Volume Rendering • Each tree node stores the mean value of the encompassed voxels • Alternatively, each tree node stores a subvolume of reduced resolution • Each tree node also stores an error measure: e.g. standard deviation of the voxels (root mean square to the approximated value)

  16. Octrees for Volume Rendering • At run time, the error measures stored in the octree nodes are compared with a threshold • The traversal is stopped at the tree nodes that pass the threshold … … …

  17. Octrees for Volume Rendering Example:

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