E E valuating Motion Constr valuating Motion Constr aints aints - - PowerPoint PPT Presentation

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E E valuating Motion Constr valuating Motion Constr aints aints - - PowerPoint PPT Presentation

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CHI 2008 Florence, Italy E E valuating Motion Constr valuating Motion Constr aints aints for 3D Wayfinding in Imme r sive and De sktop Vir tual E E nvir nvir onme nts onme nts Niklas Elm qvist M. Eduard Tudoreanu Philippas

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SLIDE 1

CHI 2008 – Florence, Italy

E valuating Motion Constr aints E valuating Motion Constr aints for 3D Wayfinding in Imme r sive and De sktop Vir tual E nvir

  • nme nts

E nvir

  • nme nts

Niklas Elm qvist elm@lri.fr

  • M. Eduard Tudoreanu

metudoreanu@ualr.edu Philippas Tsigas tsigas@chalmers.se g

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SLIDE 2

T he Challe nge T he Challe nge

vs

  • 3D motion constraints!

2

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SLIDE 3

Outline Outline

  • Problem
  • Problem
  • Design space: Wayfinding in 3D

S l ti M ti t i t

  • Solution: Motion constraints
  • User study
  • Results and discussion
  • Conclusions and future work
  • Conclusions and future work

3

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SLIDE 4

Pr

  • ble m

Pr

  • ble m
  • W ayfinding: navigation to solve
  • W ayfinding: navigation to solve

specific task

– Performed on cognitive map – Performed on cognitive map – Poor map leads to poor performance

Obje ti e t fi di b

  • Objective: support wayfinding by

aiding cognitive map building

d d – Motion constraints and guides – Exam ple: sightseeing tour of new city

4

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SLIDE 5

Vir tual vs Physic al Wor lds Vir tual vs. Physic al Wor lds

  • Why is wayfinding more difficult in
  • Why is wayfinding more difficult in

virtual worlds?

– Low visual fidelity – Low visual fidelity – Mouse and keyboard poorly mapped to 3D navigation 3D navigation – Lack of sensorial cues

  • High cognitive load on users
  • High cognitive load on users

5

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SLIDE 6

R e duc ing Cognitive L

  • ad

R e duc ing Cognitive L

  • ad
  • Method: Immersive

et

  • d

e s e Virtual Reality

– Full 3D input F ll 3D – Full 3D output

  • But: No widespread

use expensive (?) use, expensive (?)

  • Mouse and keyboard

are standard

– Even for 3D games!

6

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SLIDE 7

Cognitive Maps Cognitive Maps

Montmar tr e Montmar tr e Air por t (CDG)

?

Notr e Dame L

  • uvr

e T

  • ur

E iffe l Hote l

7

Hote l

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SLIDE 8

Suppor ting Cognitive Maps Suppor ting Cognitive Maps

  • Global coverage
  • Global coverage

– Expose viewer to whole environment

Continuous motion

  • Continuous motion

– Support spatial relations

  • Local control

– Learning by doing

Montmar tr e Air por t (CDG)

?

T

  • ur

E iffe l Notr e Dame L

  • uvr

e

8

Hote l

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SLIDE 9

3D Motion Constr aints 3D Motion Constr aints

  • Tour-based motion constraints
  • Tour based motion constraints
  • Spring-based control

S th i ti

  • Smooth animation

9

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SLIDE 10

Use r Study Use r Study

  • Predictions

Predictions

– P1 : Guiding navigation helps wayfinding – P2 : User control will improve familiarization p – P3 : More improvement for desktop

  • Controlled experiment
  • Two experiment sites
  • 35 participants

p p

– 16 (4 female) on desktop computer – 19 (2 female) on CAVE system

10

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SLIDE 11

E xpe r ime ntal Conditions E xpe r ime ntal Conditions

  • Platform ( BS) : desktop or CAVE
  • Platform ( BS) : desktop or CAVE
  • Navigation ( BS/ W S) : free, follow,

spring spring

  • Scenario ( W S) : outdoor, indoor,

i f t infoscape, conetree

  • Collect distance, error, and time

11

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SLIDE 12

Pr

  • c e dur

e Pr

  • c e dur

e

  • Phase I : Familiarization

Phase I : Familiarization

– Create cognitive map (5 minutes) – Supported by guidance technique Supported by guidance technique – Three target object types

  • Phase I I : Recall

Phase I I : Recall

– Locate two targets on overhead map

  • Phase I I I : Evaluation

Phase I I I : Evaluation

– Collect target in world – No navigation guidance

12

No navigation guidance

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SLIDE 13

R e sults R e sults

  • Navigation m ethod:
  • Navigation m ethod:

– Free navigation: CAVE better Motion constraints: desktop significantly – Motion constraints: desktop significantly better (p < 0.05)

recall distance

0 2 0,25 0,3

  • r

evaluation error

0 2 0,25 0,3

  • r

0,05 0,1 0,15 0,2 normalized erro CAVE Desktop 0,05 0,1 0,15 0,2 normalized erro CAVE Desktop

13

free follow spring free follow spring

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SLIDE 14

R e sults (c ont’d) R e sults (c ont d)

  • Desktop platform :
  • Desktop platform :

– Spring-based guidance gave better accuracy than other methods accuracy than other methods – Navigation guidance more efficient than none none

average time per target

35 40 45 50 econds) 5 10 15 20 25 30 time per target (se CAVE Desktop

14

free follow spring

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SLIDE 15

Disc ussion Disc ussion

  • Unaided navigation easier in CAVE
  • Unaided navigation easier in CAVE
  • Guidance improved performance (P1)

G id d iti l d – Guidance reduces cognitive load

  • Local control improved accuracy (P2)

– Learning by doing works for desktops

  • CAVE performed worse with guidance

p g

– Motion constraints work against – Partial confirmation of P3

15

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SLIDE 16

Conc lusions and F utur e Wor k Conc lusions and F utur e Wor k

  • Navigation guidance based on tours
  • Navigation guidance based on tours

– Improve cognitive map building Improve visual search – Improve visual search

  • Evaluation on desktop and CAVE

d d k – Navigation guidance on desktop

  • utperforms CAVE

L f i t ti h i – Less focus on interaction mechanics

16

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SLIDE 17

Que stions? Que stions?

  • Main findings:
  • Contact inform ation

Main findings:

– Free-flight best on immersive platforms

– Niklas Elmqvist (elm@lri.fr)

– Motion guidance helped desktop users

  • utperform CAVE

– Edi Tudoreanu (metudoreanu@ualr.fr)

  • utperform CAVE

users – Allowing local

– Philippas Tsigas (tsigas@chalmers.se)

deviations improved correctness for desktop

17

desktop