Associate Prof. Michael Wimmer. TU Wien

Transcription

1 Spielewelten: lt Die 3D Technologien der Zukunft Associate Prof. Michael Wimmer Institut für Computergraphik und Algorithmen TU Wien

2 Real-Time Rendering: Team & Projects Gameworld Terapoints TreeLumination GPVis 2 Desiree

3 Core Real-Time Rendering Disciplines Graphics hardware is never fast enough Real-time rendering research strives ti to reduce hidden complexity Visibility Levels of detail Image-based rendering increase visible quality Shadows Precomputed radiance transfer Real-time global illumination Vienna University of Technology 3

4 But also: New Major Challenges How to exploit parallel programming model? How to cope with demand for larger worlds? Generation (provide complexity) Display Animation How to incorporate perception? Selective rendering (redefine visible ) 4

5 Rendering and Animation Institute of Computer Graphics and Algorithms Vienna University i of Technology

6 Geometry, Surface Rendering 6

7 Illumination Precompute and store translucency efficiently, i use it at runtime for illumination [EGSR 2007]

8 Shadows Pixel Exact Shadow Mapping Use temporal integration Estimate confidence in shadow test [EGSR 2007]

9 Levels of Detail Frame-sequential LOD blending 9

10 Videos

11 Visibility Institute of Computer Graphics and Algorithms Vienna University i of Technology

12 Visibility Culling Exploit hardware occlusion queries Based on temporal coherence [Eurographics 2008]

13 ? Urban Visualization

14 Global Visibility Precomputation Visibility analysis tool Input Model Render cost distribution for 1 view cell Render cost per view cell

15 Perception Visual Attention Predictor Analyze eye-tracking data Derive statistical importance for selective rendering 15

16 Procedural Modeling 2 [Siggraph 2008]

17 Importance of Virtual Urban Envrionments urban planning

18 Importance of Virtual Urban Envrionments movies and computer games

19 Importance of Virtual Urban Envrionments cultural heritage

20 Traditional Approach Manual Modeling Source: magnusmax.com/blenderscreensnapz001.jpg

21 Traditional Approach Manual Modeling Direct control over every aspect Realize artists vision Incorporate domain knowledge

22 Traditional Approach Manual Modeling Expensive: Up to 3 weeks to model for one building Difficult to adapt and reuse

23 Motivation Urban planning, architecture needs 3D models! Previous work Manual modeling Shape grammars

24 Direct Control

25 Visual Editing

26 Subproblem Persistence Markus Lipp, Peter Wonka and Michael Wimmer 26

27 Subproblem Persistence Markus Lipp, Peter Wonka and Michael Wimmer 27

28 Subproblem Persistence Markus Lipp, Peter Wonka and Michael Wimmer 28

29 Subproblem Persistence Solution: store changes separately rulebase 1 generation change input rulebase 2 change& locators query generation store Markus Lipp, Peter Wonka and Michael Wimmer 29

30 Big Picture? click automatically create instance locator: search on slider intersect and search change: set highlight selection t (row=10, occurring mouse store shapes type ray locator col=*) parameters with in matching menu and shapes change locator Markus Lipp, Peter Wonka and Michael Wimmer 30

31 Direct Artist Control for Procedural City Layouts

32 Problem Statement No clear hierarchy, locators will not work Different approach for editing and persistence 32

33 Editing Operations Move streets May create invalid layouts Solve this automatically! 33

34 Editing Operations Merge layouts Allows multiple artists to work on one project During street move and merge: maintain persistence! 34

35 Layers Multiple urban layouts on different layers Merging operator creates final layout Non-topological transform, merging and p g g g layers enable all mentioned editing operations

36 Combined Approach Move streets: non-topological transform

37 Combined Approach Content merging: Layers+flexible merging

38 Combined Approach Persistence: move to separate layer

39 Computational Archeology Large-Scale Visualization 2

40 Ephesos: ~500M points Several 100 scan positions Several campains Scanner: up to 800m range!

41

42 Stephansdom: 262M Points Direct visualization and editing of huge point clouds Stephansdom: 100 scans 262M points 4GB data Out-of-core!

43 Domitilla: 1.2 billion points/1200 scans

44 Motivation Accurate reconstruction infeasible Archeologists need to work with real data Virtual anastylosis Measuring Scan campaign management Direct point visualization! Ignore topology and surfaces Work with noisy data

45 Data Structure Octree of Nested regular grids Optimized i for rendering on GPU

46 Rendering Estimate a closed surface on scanned objects Point Size 1 pixel Weighted Point Size

47 Selection Brush follows surface of the model

48 Selection Selection octree

49 End Thank you for your attention!