“Occlusion”
Prepared by: Shreya Rawal
1
Extending Distortion Viewing from
2D to 3D
S. Carpendale, D. J. Cowperthwaite and F. David Fracchia
(1997)
2
What after developing visualization?
Exploration
Navigation
Interpretation of data



We will be applying techniques which are used in 2D into
3D for exploration/navigation/interpretation.

3
Various viewing techniques for 3D data
Viewing angle (rotation)
Viewing position (navigation)
Combination of the two



4
Problems associated
Loss of context
Loss of orientation
“Occlusion”



5
What is detail-in-context distortion?
You provide details but keep the context intact.
Distortion: Spatial reorganization of an existing
representation


Main aim is to minimize occlusion
 Applied with
Magnification + Displacement


6
160 Nodes
Two – dimensional distortion patterns
Stretch orthogonal
Nonlinear orthogonal
Nonlinear radial
Step orthogonal




7

2D Displacement + Magnification
Stretch orthogonal
Stretching all data
on either of the two
axes centered a the
focus.
•
Compressing the
remaining areas
uniformly.
•
8

2D Displacement + Magnification
Nonlinear
orthogonal
Focus is magnified
to requested
amount.
•
Magnification
decreases according
to some function.
•
Disadvantages:
Limits the
magnification in focal
region
•
Causes more
extreme
compression at the
edges
•
9

2D Displacement + Magnification
Nonlinear radial
10
•
Adjacent edges
curve away from
the focus.
•
Outer rows of
the grid is hardly
affected.

2D Displacement + Magnification
Step Orthogonal
Data is aligned with
the focus
unstretched.
•
Less data
distortion.
•
Disadvantage:
Leaves unused
space.
•
Causes grouping of
the data.
•
11
Displacement + Magnification

2D

3D
12
Magnification + Displacement vs.
Displacement only in 2D
2D Displacement +
Magnification
•
Stretch
orthogonal
Non-Linear
orthogonal
Non-linear
Radial
Step
Orthogonal
2D only
Displacement
•
In 2D: Magnification + Displacement has the same effect as
Displacement only
13
Magnification + Displacement vs.
Displacement only in 3D
Magnification +
Displacement

Stretch
orthogonal
Non-Linear
orthogonal
Non-linear
Radial
Step
Orthogonal
OnlyDisplacement

In 3D: Displacement only had better effects than Magnification +
Displacement
14
Visual Access Distortion


Naïve 2D
3D extension still does not solve Occlusion
problem completely
Solution




move geometry according to viewpoint
magnify focus only
displace items in a different way (curves vs. straight lines)
Focus + context approach
15
Visual Access Distortion
16
Single Focus
17
Multiple Foci
18
Randomly positioned nodes:

Close to real data.
19
EdgeLens: An interactive Method for
Managing Edge Congestion in Graphs
N. Wong, S. Carpendale, S. Greenberg (2003)
20
Problems in Graph representation

When dealing with complex and large real world dataset


Many interconnected nodes leads to Edge-congestion
Edge-congestion results in:



21
obscuring nodes
obscuring individual edges
obscuring visual information
Managing edge
layout
Airline routes from NorthWest Airlines,
November, 2001
22
1.
Edge density
2.
Crossovers
3.
Occlusion
Edge congestion problem

Although position of node add value to visualization they
introduce ambiguity (edge occlusion).
Possible
interpretations
A simple 3 node graph
23
Solutions: Edge congestion problem

Layout


24
Position of nodes have
importance.
Curving edges globally
Solutions: Edge congestion problem

Filtering

Removing unimportant edges


25
only works where we can distinguish between important and
unimportant edges.
you loose the relation of one edge with other edges
Solutions: Edge congestion problem

Magnification:
26
EdgeLens: An interactive technique

It moves edges without detaching it from node





Use displacement only
Respects the semantics of node layout.
Disambiguates edge overlapping
Disambiguates node overlapping
Clarifies details about graph structure
27
Two EdgeLens approaches

Bubble Vs Spline
a) Bubble
28
b) Spline
User Study




16 participants
Task: 8 route finding task (easy, medium-easy, medium and
hard)
Post session Questionnaire
Data:



nodes: Canadian cities
edges: Airline routes
Result:

29
Spline turned out to be
better
Algorithm
Curved Edge
Original position of edge
• Decide which edges affected
• Calculate displacements
• Calculate spline control points (c1, c2)
• Draw curves
30
Features and Demo

Video
31
Discussion





Scalability of multiple focus points for technique discussed
in 1st paper (distortion viewing) as compared to
EdgeLens.
Distortion viewing (in 1st paper) can be applied to all
kinds of 3D visualizations.
Can Occlusion be completely avoided in 3D?
Deal Occlusion or Get rid of Occlusion?
Detail in context!! (Bubble vs. Spline)
32
References




S. Carpendale, D.J. Cowperthwaite, F. David Fracchia. Extending
Distortion Viewing from 2D to 3D. IEEE Computer Graphics and
Applications, 17(4), pp. 42-51, July / August 1997.
Nelson Wong, Sheelagh Carpendale and Saul Greenberg. EdgeLens:
An Interactive Method for Managing Edge Congestion in Graphs. In
Proceedings of IEEE Symposium on Information Visualization (InfoVis
2003). IEEE Press, pages 51-58, 2003
http://innovis.cpsc.ucalgary.ca/Research/EdgeLens
http://www.cs.ubc.ca/~tmm/courses/cpsc533c-06-fall/slides/depth4x4.pdf
33
My Project:

Erlang trace data:

nodes: processes
edges: interaction between processes (message sending and
spawning)

Position of nodes does not have any significance



34
Hence concept of EdgeLens might not be applicable
Yes, node occlusion and edge congestion is an issue