Depth Perception and Visualization Matt Williams From http

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

Depth Perception and Visualization References and borrowed images: n n n n 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/8 Depth. ppt(no author provided) Robertson, G. , Mackinlay, J. , &Card, S. Cone. Trees: Animated 3 D 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. 2

Depth Perception and Visualization Depth Perception n u u n Cues How do we combine these cues to perceive depth Info. Vis Application u u Which cues are helpful? Which cues may be important in your project? 3

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

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

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

Perspective Cues n n n Parallel lines converge Distant objects appear smaller Textured Elements become smaller with distance n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann. 7

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

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

Perspective Cues n n n Size Constancy Perception of actual size versus retinal size. Can perceive 2 D picture plane size for sketchy images (see below) http: //www. wlu. ca/~wwwpsych/tsang/8 Depth. ppt 10

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

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

Perspective Cues n n Usually we percieve images on the computer from the wrong viewpoint Robustness of linear perspective (Kubovy, 1986) u n e. g Movie Theatre Why might we want to correct for viewpoint changes (head movement) anyway? n n Motion Parallax Placement of virtual hand or object 13

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

Occlusion n The strongest depth cue. http: //www. wlu. ca/~wwwpsych/tsang/8 Depth. ppt n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann 15

Depth of Focus n n n Strong Depth Cue Must be coupled with user input (e. g. point of fixation) Computationally expensive n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann. 16

Cast Shadows n n n Important cue for height of an object above a plane An indirect depth cue Shown to be stronger than size perspective (Kersten, 1996) 17 n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

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

Eye Convergence n Better for relative depth than for absolute depth n. Ware, 20 C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

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

Stereoscopic Depth n n Panum’s fusional area Range before diplopia occurs(worst case): u u n Fovea – 1/10 of a degree (3 pixels) Periphery – 1/3 of a degree (10 pixels) Factors for Fusion u u Moving images Blurred images Size Exposure 22

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

Stereoscopic Depth n n Problems with stereoscopic displays Diplopia occurs when images don’t fuse (try it) u u n Vergence Focus Problem u u n Diplopia reduced for blurred images – great for the real world but … Stereoscopic displays only contain sharp images. Closeup unattended items can be obtrusive. Everything on the computer screen is on the same focal plane. Causes eyestrain Frame Cancellation: 26

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

Stereoscopic Displays n Cyclopean Scale u Move virtual environment close to the display plane « No Cancellation « Reduced Vergence-focus problem 28 n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Stereoscopic Displays n n n 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 29

Depth Perception Theory n General Unified Theory u u u Perceived Depth = Weighted sum of all Depth Cues Rank the cues in importance e. g. Occlusion « Motion Parallax « Stereo « Size constancy « Etc. « 30

Depth Perception Theory n Importance changes with distance Depth Contrast Motion parallax Occlusion Cast Shadows Stereo Size constancy , 96 Convergence Aerial 1 10 Depth (meters) 100 Cutting, 1996 31

Space Perception Theory n Task Dependant Model u Cues weights are combined differently based on the task u 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 32 Wanger, 1992

Info. Vis Tasks: n n n n Tracing 3 D data paths Judging 3 D surfaces Finding 3 D patterns of points Relative Position in 3 D space Judging movement of Self Judging Up Direction Feeling a “sense of Presence” 33

Tracing 3 D Data Paths Benefits of 3 D Trees n u More nodes can be displayed (Robertson et al. , 1993) u Reduced errors in detecting Paths (Sollenberger and Milgram, 1993) 34 n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann.

Tracing 3 D Data Paths n Beneficial Cues: Kinetic Depth and Stereoscopic Depth reduced errors in path detection u Kinetic Depth was the stronger cue u Occlusion Is helpful u u (Ware and Franck, 1996) 35

3 D Patterns of Points http: //neutrino. kek. jp/~kohama/sarupaw_html/fig/nt_3 d. gif http: //www-pat. fnal. gov/nirvana/plot_wid. html 38

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

3 D Patterns of Points Add shape to clouds of points n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann. 40

Judging Relative Position n Small Scale (Threading a needle) u u n Beneficial: Stereo Not Beneficial: Motion Parallax Large Scale ( > 30 m) u u Beneficial: motion parallax, perspective, cast shadows, texture gradients Not Beneficial: stereo n. Ware, C. , Chapter 8 of Information Visualization: Perception for Design. 2000, San Fancisco: Morgan Kaufmann. 41

Depth Cues Existing Theories Application to Info. Vis Conclusion n n n n Occlusion Texture Gradient Size Constancy Cast Shadows Stereo From: http: //www. cs. washington. edu/homes/cassidy/tele/index. html 44