1. Self-organizing tree models
Based on
Palubicki et al., “Self-oganizing tree models for image synthesis” in SIGGRAPH 2009
As part of
CS 658 Mike Jones

2. Your reading What questions and comments did you have for this paper?
What problem are they trying to solve?
What have others done and why is that inadequate?
How did they solve the problem?
How is their solution a solution?
Did you believe it? What’s next?

3. Objectives After class today you should be able to
Compare Palubicki’s space colonization algorithm with Runion’s.
Outline all of the factors and parameters which go into creating a tree in Palubicki’s model.
Argue for or against Palubicki’s model in a specific hypothetical use scenario.

4. The Problem
Genetic branching patterns are part but not all of the story.
Nature vs. Nurture

5. Their Solution Self-organization
This is more important than I thought on first reading.
Architectural and self-organizing components
Architectural: form determined by branching pattern
Self-organizing: structure arises from individual “decisions” (fates in this case)
Signaling
Procedural brushes

6. Self-organization
Mandelbrot’s fractal tree on the left. Note that branch segments get smaller with each generation. (Generation given by color).
Ulam’s approach on the right. Branch segment length is constant, but buds have different fates. Buds that might collide with the tree die.
The fact that bud fates change based on “decisions” based on local conditions is what makes this self-organizing.
This isn’t new in tree modeling but “they advance[] this class of models beyond the visually rudimentary models in previous work.”
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

7. User Control
“difficult to model older trees with irregular but harmoniously balanced crowns.” Self-organization within a confined space solves this problem.
Quote from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009
Image from Reeves and Blau “Approximate and probabilistic algorithms for shading and rendering structured particle systems” in SIGGRAPH 1985

8. Vocabulary Monopoidal Sympoidal Endogenous Exogenous Acropetal
Basipetal
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

9. Space Colonization
“The key modification is the introduction of buds as the only locations that can produce new branches.”
Delete markers in the occupancy zone
Move in average direction toward markers in the perception volume.
Theta, rho and r set by user.
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

10. Shadows Q = max (C – s + a, 0) Q is light exposure
C = all available light.
Shadow contributed by a single bud is
a/(b^q) where
a = user defined parameter
b = user defined parameter
q = depth under the bud
Sum up all the shadow contributions in each voxel to get the light distribution in the crown. q
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

11. Bud Fate Sylleptic is the simplest case. Why?
Apical control is required to obtain excurrent forms. Why?
Sylleptic: new branches in the same season as the supporting stem was created.
Proleptic: new branches in the next year after supported stem was created
Sympoidal: development moves to lateral branches and axial bud dies or becomes a flower
Excurrent form: main trunk.
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

12. Apical Control: Extended BH
Originally purely endogenous.
Is their approach purely endogenous?
User sets alpha and lambda
Q is the light resource
V is the growth inducing hormone
Metamers = floor(v), internode lengths = v/n
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

13. Apical control: priority model
What the difference in hormone distribution between extended BH and the priority model?
= 2.1
= 1.1
= 3.2
Average exposure per bud is used to seed the priority model.
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

14. Apical control: priority model
Grey circles are priorities within different axes.
Vertical axis is the primary axis.
Top distribution is based only on light exposure
Bottom distribution has apical control
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

15. Apical control: priority model
N = number of buds supported by that axis.
V_i = hormone going to bud i on the priority list.
V = amount of hormone going into the axis.
W = weight (see function below)
Q_i = light at bud i.
As before, v_base = alpha Q_base.
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

16. Apical control: priority model
N = number of buds supported by that axis.
V_i = hormone going to bud i on the priority list.
V = amount of hormone going into the axis.
W = weight (see function below)
Q_i = light at bud i.
As before, v_base = alpha Q_base.
What the difference in hormone distribution between extended BH and the priority model?
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

17. What would it take to grow a tree?

18. Shedding Branches
If a branch is too shaded, it can fall off.

19. Sketching Trees
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

20. Results
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

21. Results
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

22. Results
Image from Palubicki et al. “Self-organizing tree models in image synthesis” in SIGGRAPH 2009

23. Benes’ Model

24. Wrap-up How does Palubicki’s model compare to Benes’ model?
How does Palubicki’s model compare to Runions 07 model?
How does Palubicki’s model compare to Reeves and Blau?