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Page 1: Depth Perception and Visualization Matt Williams From: .

Depth Perception and Visualization

Matt Williams

From: http://www.cs.washington.edu/homes/cassidy/tele/index.html

Page 2: Depth Perception and Visualization Matt Williams From: .

Depth Perception and Visualization References and borrowed images: Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San

Fancisco: Morgan Kaufmann. J.D. Pfautz, Depth Perception in Computer Graphics, Doctoral Dissertation,

University of Cambridge, UK, 2000. C. Ware, C. Gobrecht, and M.A. Paton, "Dynamic Adjustment of Stereo Display Parameters,"

IEEE Transactions on Systems, Man and Cybernetics---Part A: Systems and Humans, Vol. 28, No. 1, Jan. 1998, pp. 56-65.

www.wlu.ca/~wwwpsych/tsang/8Depth.ppt(no author provided) Robertson,G.,Mackinlay,J.,&Card,S.ConeTrees: Animated 3D visualizations of hierarchical

information. In Proceedings of CHI'91 (New Orleans, LA), ACM, 189-194. WANGER, L., FERWANDA, J., AND GREENBERG, D. 1992. Perceiving spatial relationships in

computer generated images. IEEE Computer Graphics and Applications (May) 44-58.

Page 3: Depth Perception and Visualization Matt Williams From: .

Depth Perception and Visualization

Depth Perception Cues How do we combine these cues to perceive

depth InfoVis Application

Which cues are helpful? Which cues may be important in your

project?

Page 4: Depth Perception and Visualization Matt Williams From: .

Depth Cues Monocular

Perspective Cues Size Occlusion Depth of Focus Cast Shadows Shape from Motion

Binocular Eye Convergence Stereoscopic depth

Page 5: Depth Perception and Visualization Matt Williams From: .

Structure from Motion Motion Parallax Kinetic Depth

n

aa

ba

c

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 6: Depth Perception and Visualization Matt Williams From: .

Structure from Motion Kinetic Depth Effect Assumption of rigidity allows us to

assume shape as objects move/rotate

aa

ba

c

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 7: Depth Perception and Visualization Matt Williams From: .

Perspective Cues Parallel lines converge Distant objects appear smaller Textured Elements become smaller

with distance

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 8: Depth Perception and Visualization Matt Williams From: .

Perspective Cues

http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Page 9: Depth Perception and Visualization Matt Williams From: .

Perspective Cues Taking advantage of linear perspective

in visualization

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 10: Depth Perception and Visualization Matt Williams From: .

Perspective Cues Size Constancy Perception of actual size versus retinal size. Can perceive 2D picture plane size for sketchy

images (see below)

http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Page 11: Depth Perception and Visualization Matt Williams From: .

Perspective Cues

http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Page 12: Depth Perception and Visualization Matt Williams From: .

Perspective Cues

http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Page 13: Depth Perception and Visualization Matt Williams From: .

Perspective Cues Usually we percieve images on the

computer from the wrong viewpoint Robustness of linear perspective (Kubovy, 1986)

e.g Movie Theatre

Why might we want to correct for viewpoint changes (head movement) anyway?

Motion Parallax Placement of virtual hand or object

Page 14: Depth Perception and Visualization Matt Williams From: .

Perspective Cues Placement of virtual hand or object Need for head coupled perspective

vrlab.postech.ac.kr/vr/gallery/edu/vr/display.ppt

Page 15: Depth Perception and Visualization Matt Williams From: .

Occlusion The strongest depth cue.

http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 16: Depth Perception and Visualization Matt Williams From: .

Depth of Focus Strong Depth Cue Must be coupled with user input (e.g.

point of fixation) Computationally expensive

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 17: Depth Perception and Visualization Matt Williams From: .

Cast Shadows Important cue for height of an object above a

plane An indirect depth cue Shown to be stronger than size perspective

(Kersten, 1996)

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 18: Depth Perception and Visualization Matt Williams From: .

Shape From Shading

Ware Chapter 7http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 19: Depth Perception and Visualization Matt Williams From: .

Eye Convergence

Better for relative depth than for absolute depth

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 20: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Depth How it works Two different views fuse to one

perceived view (try it)

aa

Right eyeScreen

Left eye

Panum's Fusional Area

disparity = -

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 21: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Depth Panum’s fusional area Range before diplopia occurs(worst case):

Fovea – 1/10 of a degree (3 pixels) Periphery – 1/3 of a degree (10 pixels)

Factors for Fusion Moving images Blurred images Size Exposure

Page 22: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Depth

velab.cau.ac.kr/lecture/Stereo.ppt

Page 23: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Depth Problems with stereoscopic displays Diplopia occurs when images don’t fuse (try it)

Diplopia reduced for blurred images – great for the real world but …

Stereoscopic displays only contain sharp images. Close-up unattended items can be obtrusive.

Vergence Focus Problem Everything on the computer screen is on the same focal

plane. Causes eyestrain

Frame Cancellation:

Page 24: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Depth Frame Cancellation:

Solution?

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 25: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Displays Cyclopean Scale

Move virtual environment close to the display plane

No Cancellation Reduced Vergence-focus problem

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 26: Depth Perception and Visualization Matt Williams From: .

Stereoscopic Displays Virtual Eye Separation

(Telestereoscope) Allows for a decrease or

increase in disparity Allows for an increase or

decrease in the depth of the virtual environment

http://www.cs.washington.edu/homes/cassidy/tele/index.html

Page 27: Depth Perception and Visualization Matt Williams From: .

Depth Perception Theory General Unified Theory

Perceived Depth = Weighted sum of all Depth Cues Rank the cues in importance e.g.

Occlusion Motion Parallax Stereo Size constancy Etc.

Page 28: Depth Perception and Visualization Matt Williams From: .

Depth Perception Theory Importance changes with distance

, 96, 96

Cutting, 1996

Depth

Con

trast

Depth (meters)

Occlusion

1 10 100

Size constancy

Cast Shadows

Stereo

Motionparallax

Convergence

Aerial

Page 29: Depth Perception and Visualization Matt Williams From: .

Space Perception Theory Task Dependant Model

Cues weights are combined differently based on the task Evidence?

Task: Orientation of a virtual Object• Cast Shadows and Motion Parallax help

• But …Linear Perspective hinders such orientation Task: Object translation

• Linear perspective was the most useful cue

Wanger, 1992

Page 30: Depth Perception and Visualization Matt Williams From: .

InfoVis Tasks: Tracing 3D data paths Judging 3D surfaces Finding 3D patterns of points Relative Position in 3D space Judging movement of Self Judging Up Direction Feeling a “sense of Presence”

Page 31: Depth Perception and Visualization Matt Williams From: .

Tracing 3D Data Paths Benefits of 3D Trees

More nodes can be displayed (Robertson et al., 1993)

Reduced errors in detecting Paths (Sollenberger and Milgram, 1993)

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 32: Depth Perception and Visualization Matt Williams From: .

Tracing 3D Data Paths

Beneficial Cues: Kinetic Depth and Stereoscopic

Depth reduced errors in path detection

Kinetic Depth was the stronger cue Occlusion Is helpful (Ware and Franck, 1996)

Page 33: Depth Perception and Visualization Matt Williams From: .

3D Patterns of Points

http://www-pat.fnal.gov/nirvana/plot_wid.htmlhttp://neutrino.kek.jp/~kohama/sarupaw/sarupaw_html/fig/nt_3d.gif

Page 34: Depth Perception and Visualization Matt Williams From: .

3D Patterns of Points

Beneficial Cues: Structure from motion Stereo Depth

Not Beneficial: Perspective Size Cast Shadows Shape from Shading (How?)

Page 35: Depth Perception and Visualization Matt Williams From: .

3D Patterns of Points

Add shape to clouds of points

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 36: Depth Perception and Visualization Matt Williams From: .

Judging Relative Position

Small Scale (Threading a needle) Beneficial: Stereo Not Beneficial: Motion Parallax

Large Scale ( > 30 m) Beneficial: motion parallax,

perspective, cast shadows, texture gradients

Not Beneficial: stereo

Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Page 37: Depth Perception and Visualization Matt Williams From: .

Conclusion Depth Cues Existing Theories Application to InfoVis

Occlusion Texture

Gradient Size Constancy Cast Shadows Stereo

From: http://www.cs.washington.edu/homes/cassidy/tele/index.html


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