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Depth Perception and Visualization Matt Williams From:

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1 Depth Perception and Visualization Matt Williams From: http://www.cs.washington.edu/homes/cassidy/tele/index.html

2 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.

3 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?

4 Depth Cues Monocular  Perspective Cues  Size  Occlusion  Depth of Focus  Cast Shadows  Shape from Motion Binocular  Eye Convergence  Stereoscopic depth

5 Structure from Motion Motion Parallax Kinetic Depth n Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

6 Structure from Motion Kinetic Depth Effect Assumption of rigidity allows us to assume shape as objects move/rotate Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

7 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.

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

9 Perspective Cues Taking advantage of linear perspective in visualization Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

10 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

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

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

13 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

14 Perspective Cues Placement of virtual hand or object Need for head coupled perspective vrlab.postech.ac.kr/vr/gallery/edu/vr/display.ppt

15 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.

16 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.

17 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.

18 Shape From Shading Ware Chapter 7 http://www.wlu.ca/~wwwpsych/tsang/8Depth.ppt Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

19 Atmospheric Depth Reduction in contrast of distant objects Exaggerated in 3D displays using what is called proximity luminance covariance. Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

20 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.

21 Stereoscopic Depth How it works Two different views fuse to one perceived view (try it) Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

22 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

23 Stereoscopic Depth velab.cau.ac.kr/lecture/Stereo.ppt

24 Stereoscopic Depth velab.cau.ac.kr/lecture/Stereo.ppt

25 Stereoscopic Depth Horopter The arc upon which everything is in the same location on both retinal images

26 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:

27 Stereoscopic Depth Frame Cancellation: Solution? Ware, C., Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

28 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.

29 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

30 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.

31 Depth Perception Theory Importance changes with distance, 96 Cutting, 1996 Depth Contrast Depth (meters) Occlusion 110100 Size constancy Cast Shadows Stereo Motion parallax Convergence Aerial

32 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

33 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”

34 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.

35 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)

36 Tracing 3D Data Paths  Cone Trees  (Robertson, 1993)

37 Judging 3D surface structure Judging height, gradient, curvature etc. Judging height of Cones  Stereo depth > Structure from motion (Durgen et al, 1995) Judging the gradient of textured surface  Structure from motion > Stereo (Tittle et al., 1995) The importance of Stereo Depth, Kinetic Depth, Shape from Shading, Surface Textures is situation dependant. Conclusion: Arbitrary surfaces - Include them all

38 3D Patterns of Points http://www-pat.fnal.gov/nirvana/plot_wid.html http://neutrino.kek.jp/~kohama/sarupaw/sarupaw_html/fig/nt_3d.gif

39 3D Patterns of Points Beneficial Cues:  Structure from motion  Stereo Depth Not Beneficial:  Perspective  Size  Cast Shadows  Shape from Shading (How?)

40 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.

41 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.

42 Movement of Self (Vection) Parameters: Field Size Background motion (Train example) Stereo will help determine if something is perceived as background

43 Feeling of Presence Beneficial Parameters:  High frame rate  High level of detail Not Beneficial:  Stereo (did not add to realism) (Hendrix and Barfield, 1996)

44 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

45 Discussion Projects?? Monocular  Perspective Cues  Size  Occlusion  Depth of Focus  Cast Shadows  Shape from Motion Binocular  Eye Convergence  Stereoscopic depth

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