Depth Perception and Visualization Matt Williams From: .

transcript

Depth Perception and Visualization

Matt Williams

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

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.

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?

Depth Cues Monocular

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

Binocular Eye Convergence Stereoscopic depth

Structure from Motion Motion Parallax Kinetic Depth

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

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

assume shape as objects move/rotate

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

with distance

Perspective Cues

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

Perspective Cues Taking advantage of linear perspective

in visualization

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

images (see below)

Perspective Cues

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

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

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

Occlusion The strongest depth cue.

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

point of fixation) Computationally expensive

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)

Shape From Shading

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

Eye Convergence

Better for relative depth than for absolute depth

Stereoscopic Depth How it works Two different views fuse to one

perceived view (try it)

Right eyeScreen

Left eye

Panum's Fusional Area

disparity = -

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

Stereoscopic Depth

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

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:

Stereoscopic Depth Frame Cancellation:

Solution?

Stereoscopic Displays Cyclopean Scale

Move virtual environment close to the display plane

No Cancellation Reduced Vergence-focus problem

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

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.

Depth Perception Theory Importance changes with distance

, 96, 96

Cutting, 1996

Depth (meters)

Occlusion

1 10 100

Size constancy

Cast Shadows

Stereo

Motionparallax

Convergence

Aerial

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

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”

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)

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)

3D Patterns of Points

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

Beneficial Cues: Structure from motion Stereo Depth

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

Add shape to clouds of points

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

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

Depth Perception and Visualization Matt Williams From: .

Documents