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Depth Perception and Visualization

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:

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Depth Perception and Visualization

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

  19. 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.

  20. 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.

  21. 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

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

  23. 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:

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

  25. 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.

  26. 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

  27. 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.

  28. , 96 Motion parallax Occlusion Cast Shadows Size constancy Depth Contrast Stereo Convergence Aerial 1 10 100 Depth (meters) Cutting, 1996 Depth Perception Theory , 96 • Importance changes with distance

  29. 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

  30. 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”

  31. 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.

  32. 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)

  33. 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

  34. 3D Patterns of Points • Beneficial Cues: • Structure from motion • Stereo Depth • Not Beneficial: • Perspective • Size • Cast Shadows • Shape from Shading (How?)

  35. 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.

  36. 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.

  37. 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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