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www.crs4.it/vic/ Visual Computing Group Visual Computing – UniCa, June 17th, 2015 Part 2 – Mobile graphics trends • Marco Agus • Marcos Balsa June 2015 1

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  1. 1. www.crs4.it/vic/ Visual Computing Group Visual Computing UniCa, June 17th, 2015 Part 2 Mobile graphics trends Marco Agus Marcos Balsa June 2015 1
  2. 2. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Applications Wide range of applications Cultural Heritage Medical Image 3D object registration GIS Gaming VR & AR - Building reconstruction - Virtual HCI 3D representation + additional information 2
  3. 3. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mobile 3D interactive graphics General pipeline similar to standard interactive applications DATA ACCESS RENDERING DISPLAY INTERACTION Scene 3
  4. 4. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 MOBILE DEVICE SERVER Remote rendering General solution since first PDAs DATA ACCESS RENDERING DISPLAY INTERACTION Scene NETWORK 4
  5. 5. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Remote rendering 3D graphics applications require intensive computation and network bandwidth electronic games visualization of very complex 3D scenes Remote rendering has long history and it is successfully applied for gaming services [OnLive] Limitation: interaction latency in cellular networks 6
  6. 6. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 MOBILE DEVICE SERVER Mixed Mobile/Remote rendering As mobile GPUs progress... DATA ACCESS RENDERING DISPLAY INTERACTION Scene NETWORK 7
  7. 7. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mixed Mobile/Remote rendering Model based versus Image based methods Model based methods Original models Partial models Simplified models Couple of lines Point clouds Eisert and Fechteler. Low delay streaming of computer graphics (ICIP 2008) Gobbetti et al. Adaptive Quad Patches: an Adaptive Regular Structure for Web Distribution and Adaptive Rendering of 3D Models. (Web3D 2012) Balsa et al.,. Compression-domain Seamless Multiresolution Visualization of Gigantic Meshes on Mobile Devices (Web3D 2013) Diepstraten et al., 2004. Remote Line Rendering for Mobile Devices (CGI 2004) Duguet and Drettakis. Flexible point-based rendering on mobile devices (IEEE Trans. on CG & Appl, 2004) 9
  8. 8. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mixed Mobile/Remote rendering Model based versus Image based methods Model based methods Original models Partial models Simplified models Couple of lines Point clouds Eisert and Fechteler. Low delay streaming of computer graphics (ICIP 2008) Gobbetti et al. Adaptive Quad Patches: an Adaptive Regular Structure for Web Distribution and Adaptive Rendering of 3D Models. (Web3D 2012) Balsa et al.,. Compression-domain Seamless Multiresolution Visualization of Gigantic Meshes on Mobile Devices (Web3D 2013) Diepstraten et al., 2004. Remote Line Rendering for Mobile Devices (CGI 2004) Duguet and Drettakis. Flexible point-based rendering on mobile devices (IEEE Trans. on CG & Appl, 2004) 10
  9. 9. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mixed Mobile/Remote rendering Image based methods Image impostors Environment maps Depth images Noimark and Cohen-Or. Streaming scenes to mpeg-4 video-enabled devices (IEEE, CG&A 2003) Lamberti and Sanna. A streaming-based solution for remote visualization of 3D graphics on mobile devices (IEEE, Trans. VCG, 2007) Bouquerche and Pazzi. Remote rendering and streaming of progressive panoramas for mobile devices (ACM Multimedia 2006) Zhu et al. Towards peer-assisted rendering in networked virtual environments (ACM Multimedia 2011) Shi et al. A Real-Time Remote Rendering System for Interactive Mobile Graphics (ACM Trans. On Multimedia, 2012) Doellner et al. Server-based rendering of large 3D scenes for mobile devices using G- buffer cube maps ( ACM Web3D, 2012) 11
  10. 10. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mobile visualization systems Volume rendering Point cloud rendering Moser and Weiskopf. Interactive volume rendering on mobile devices. Vision, Modeling, and Visualization VMV. Vol. 8. 2008. Noguerat al. Volume Rendering Strategies on Mobile Devices. GRAPP/IVAPP. 2012. Campoalegre, Brunet, and Navazo. Interactive visualization of medical volume models in mobile devices. Personal and ubiquitous computing 17.7 (2013): 1503-1514. Balsa et al. Interactive exploration of gigantic point clouds on mobile devices. ( VAST 2012) He et al. A multiresolution object space point-based rendering approach for mobile devices (AFRIGRAPH, 2007) 12 Rodrguez, Marcos Balsa, and Pere Pau Vzquez Alcocer. Practical Volume Rendering in Mobile Devices. Advances in Visual Computing. Springer, 2012. 708-718.
  11. 11. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mobile visualization systems Volume rendering Point cloud rendering Moser and Weiskopf. Interactive volume rendering on mobile devices. Vision, Modeling, and Visualization VMV. Vol. 8. 2008. Noguerat al. Volume Rendering Strategies on Mobile Devices. GRAPP/IVAPP. 2012. Campoalegre, Brunet, and Navazo. Interactive visualization of medical volume models in mobile devices. Personal and ubiquitous computing 17.7 (2013): 1503-1514. Balsa et al. Interactive exploration of gigantic point clouds on mobile devices. ( VAST 2012) He et al. A multiresolution object space point-based rendering approach for mobile devices (AFRIGRAPH, 2007) 13 Rodrguez, Marcos Balsa, and Pere Pau Vzquez Alcocer. Practical Volume Rendering in Mobile Devices. Advances in Visual Computing. Springer, 2012. 708-718.
  12. 12. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Trends in mobile graphics Hardware acceleration for improving frame rates, resolutions and rendering quality Parallel pipelines Real-time ray tracing Multi-rate approaches 18
  13. 13. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 SGRT: Real-time ray tracing Samsung reconfigurable GPU based on Ray Tracing Main key features: an area-efficient parallel pipelined traversal unit flexible and high- performance kernels for shading and ray generation Lee, Won-Jong, et al. SGRT: A mobile GPU architecture for real-time ray tracing. Proceedings of the 5th High-Performance Graphics Conference, 2013. Shin et al., Full-stream architecture for ray tracing with efficient data transmission, 2014 IEEE ISCAS 19
  14. 14. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Adaptive multi-rate shading The multi-rate shading pipeline rasterizes triangles into coarse fragments that correspond to multiple pixels of coverage Coarse fragments are shaded, then partitioned into fine fragments for subsequent per-pixel shading If the coarse fragment shader determines an effect should not be evaluated at low sampling rates, these computations are performed in the fine shading stage Complex pipeline scheduling reduce shading costs to more than three and sometimes up to a factor of five. He et al. Extending the graphics pipeline with adaptive, multi-rate shading. ACM Transactions on Graphics (TOG) 33.4 , 2014. 20
  15. 15. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Adaptive multi-frequency shading Real-time rendering of tessellated geometry is still fairly expensive, as current pixel shading methods, e.g. MSAA), do not scale well with geometric complexity. Pixel shading is tied to the coarse input patches and reused between triangles, effectively decoupling the shading cost from the tessellation level. AMFS can evaluate different parts of shaders at different frequencies, allowing very efficient shading. Clarberg, Petrik, et al. AMFS: adaptive multi-frequency shading for future graphics processors. ACM Transactions on Graphics (TOG) 33.4 , 2014. 21
  16. 16. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Coarse pixel shading Architecture for varying shading rates in a rasterization pipeline, while keeping the visibility sampling rate constant A single rendering pass executes shading code at one or more different rates: per group of pixels, per pixel, and per sample Vaidyanathan, Karthik, et al. Coarse Pixel Shading. Eurographics/ACM SIGGRAPH Symposium on High Performance Graphics, 2014. 22
  17. 17. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 MOBILE DEVICE Mobile rendering with capture Exploiting mobile device sensors... DATA ACCESS RENDERING DISPLAY INTERACTION Scene CAPTUREEnvironment 23
  18. 18. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 MOBILE DEVICE Mobile rendering with capture Exploiting mobile device sensors... DATA ACCESS RENDERING DISPLAY INTERACTION Scene CAPTUREEnvironment Example: Google Tango https://www.google.com/ata p/projecttango/#project 24
  19. 19. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 3D acquisition and processing Live 3D reconstruction on mobile phones The system allows to obtain 3D models of pleasing quality interactively and entirely on- device Efficient and accurate scheme for integrating multiple stereo-based depth hypotheses into a compact and consistent 3D model Kolev et al. Turning Mobile Phones into 3D Scanners (CVPR 2014) Tanskanen et al. Live Metric 3D Reconstruction on Mobile Phones (ICCV 2013) 25
  20. 20. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Physical simulations Framework for physically and chemically-based simulations of analog alternative photographic processes Efficient fluid simulation and manual process running on iPad Echevarria et al. Computational simulation of alternative photographic processes. Computer Graphics Forum. Vol. 32. 2013. 26
  21. 21. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Correcting visual aberrations Computational display technology that predistorts the presented content for an observer, so that the target image is perceived without the need for eyewear Demonstrated in low- cost prototype mobile devices Huang, Fu-Chung, et al. Eyeglasses-free display: towards correcting visual aberrations with computational light field displays.ACM Transactions on Graphics (TOG) 33.4, 2014. 27
  22. 22. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Mobile Graphics Next: Real time visualization of massive models on mobile systems 28
  23. 23. www.crs4.it/vic/ Visual Computing Group Visual Computing UniCa, June 17th, 2015 Mobile Graphics Marco Agus Marcos Balsa June 2015 Real time visualization of massive models on mobile systems 29
  24. 24. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Visualization techniques Problems Large meshes Scenes with complex lighting Volume rendering 30
  25. 25. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Our Contributions: chunked multiresolution structures Two approaches Fixed coarse subdivision Adaptive QuadPatches Multiresolution inside patch Adaptive coarse subdivision Compact Adaptive TetraPuzzles Global multiresolution 31
  26. 26. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Adaptive Quad Patches Simplified Streaming and Rendering for the Web Represent models as fixed number of multiresolution quad patches Image representation allows component reuse! Natural multiresolution model inside each patch Adaptive rendering handled totally within shaders! Works with topologically simple models Best paper, WEB3D2012 Javascript! 32
  27. 27. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 33 Generate clean manifold triangle mesh Poisson reconstruction [Kazhdan et al. 2006] Remove topological noise Discard connected components with too few triangles Parameterize the mesh on a quad-based domain Isometric triangle mesh parameterization Abstract domains [Pietroni et al. 2010] Remap into a collection of 2D square regions +Vertex in tri. Barycenter / +Quad at each edge Resample each quad from original geometry Associates to each quad a regular grid of samples (position, color and normal) -> image Multiresolution inside quad (image mip pyramid) Pre-processing (Reparameterization) Quad-based mesh Base coarse mesh
  28. 28. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Pre-processing (Multiresolution) Collection of variable resolution quad patches Coarse representation of the original model Multiresolution pyramids Detail geometry, Color, Normals Shared border information Ensure connectivity 34
  29. 29. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Adaptive rendering 1. CPU Lod Selection Different quad LOD, but must agree on edges Quad LOD = max edge LOD (available) Use finest LOD available Send VBO with regular grid (1 for each LOD) 2. GPU: Vertex Shader Snap vertices on edges (match neighbors) Base position = corner interpolation (u,v) Displace VBO vertices normal + displacement (dequantized) 3. GPU: Fragment Shader Texturing & Shading 35 0,0 u,v 1,1
  30. 30. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Rendering example Patches Levels Shading 36
  31. 31. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Results Adaptive rendering 1 pixel accuracy 37 fps (min 13 fps) Network streaming Required 312Kbps (max 2.8Mbps) for no delay ADSL 8Mbps 2 s fully refined model from scratch 37
  32. 32. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Conclusions: Adaptive Quad Patches Effective creation and distribution system Fully automatic Compact, streamable and renderable 3D model representations Low CPU overhead GPU adaptive rendering WebGL Desktop Mobile Limitations Closed objects with large components (i.e,3D scanned objs) Visual approximation (lossy) Explore more aggressive compression techniques 38
  33. 33. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Compact Adaptive Tetra Puzzles Efficient distribution and rendering for mobile 39 Built on Adaptive TetraPuzzles [CRS4+ISTI CNR, SIGGRAPH04] Regular conformal hierarchy of tetrahedra Spatially partition of input mesh Mesh fragments at different resolutions Associated to implicit diamonds Objective Mobile Limited resources (512MB-3GB ram CPU/GPU) Limited performance (CPU/GPU) Compact GPU representation Good compression ratio (maximize resource usage) Low decoding complexity (maximize decoding/rendering performance)
  34. 34. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Our approach Our contribution Geometry clipped against containing tetrahedra Barycentric coordinates used for local tetrahedra geometry reparameterization Fully adaptive and seamless 3D mesh structure with local quantization GPU friendly compact data representation 64bit = position (3 bytes) + color (3 bytes)+ normal(2 bytes) Normals encoded using the octahedron approach by [Meyer et al. 2012] Further compression for web distribution exploiting local data coherence for entropy coding Pbarycentric = 1*P1+2*P2+3*P3+4*P4 40 P2 P1 P3 P4
  35. 35. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Overview Construction Start with hires triangle soup Partition model Construct non-leaf cells by bottom-up recombination and simplification of lower level cells Assign model space errors to cells Rendering Refine graph, render selected precomputed cells Project errors to screen Dual queue Adaptive rendering On-line 41 GPU Cache Ensure continuity Shared information on borders
  36. 36. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Pre-processing (Barycentric parametrization) Tetrahedron geometry is reparameterized into barycentric coordinates Triangles clipped on tetrahedron faces Inner vertices (I) 4 tetrahedron corners Vertices lying on tetrahedron faces 3 corners of the triangle defining the face Ensure continuity between neighbors Snap vertices on boundaries (
  37. 37. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Rendering process Extract view dependent diamond cut (CPU) Required patches requested to server Asynchronous multithread client Apache 2 based server (data repository, no processing) For each node (GPU Vertex Shader): VBO containing barycentric coordinates , normals and colors (64 bits per vertex) Decode position : P = MV * [C0 C1 C2 C3] * [Vb] Vb is the vector with the 4 barycentric coords C0..C3 are tetrahedra corners Decode normal from 2 bytes encoding [Meyers et al. 2010] Use color coded in RGB24 48
  38. 38. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Results Preprocessing (on 8 cores) 24k triangles/s 40 to 50 bits/vertex Rendering (on iPad, tolerance of 3 pixels) Average 30 Mtris/s Average 37 fps 15 fps for refined views (>2M triangles budget) Rendering (on iPhone, tolerance of 3 pixels) Average 2.8 Mtris/s Average 10 fps 2.8 fps for refined views (>1M triangles budget) Streaming Full screen view 30s on wireless, 45s on 3G David 14.5MB (1.1 Mtri) St. Matthew 19.9MB (1.8 Mtri) 49
  39. 39. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Conclusions: Compact ATP Effective distribution and rendering of gigantic 3D triangle meshes on common handheld devices Compact, GPU friendly, adaptive data structure Exploiting the properties of conformal hierarchies of tetrahedra Seamless local quantization using barycentric coordinates Two-stage CPU and GPU compression Integrated into a multiresolution data representation Limitations Requires coding non-trivial data structures, hard to implement on scripting environments 50
  40. 40. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Visualization techniques Applications Large meshes Scenes with complex lighting Volume rendering 51
  41. 41. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Explore Maps Ubiquitous exploration of scenes with complex illumination Real-time limitations ~30Hz Difficulties handling complex illumination on mobile/web platforms with current methods Image-based techniques Constraining camera movement to a set of fixed camera positions Enable pre-computed photorealistic visualization 52
  42. 42. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Scene Discovery Goal Set of probes that provides full coverage of the scene Probe = 360 panoramic point of view Set of arcs connecting probes that enable full scene navigation 55 Explore Map
  43. 43. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Scene Discovery Objective: Full scene coverage !!! 1. Add new probe in unseen area 2. Optimize view move towards seen area barycenter Repeat until convergence (no improvement | no movement) < threshold 3. Goto 1 while coverage < threshold 56 Probe v0 Probe v1 Joint coverage
  44. 44. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Probe optimization Clustering Cluster representative [Markov Cluster Algorithm, MCL] Find natural clusters in graph random walk Visit probability encoded in arcs = %coverage overlap between nodes Equivalent coverage, but better browsing experience Probe synthesis Optimization using a simulated annealing approach of: coverage & perceptual metrics[Secord et al. 2011] 57
  45. 45. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Connecting Probes Search for a common visible region and choose the closest point in this region A mass-spring system is used for optimizing and smoothing this path
  46. 46. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Dataset Creation (rendering) Input: Explore Map Probes with full scene coverage Transitions between reachable probes Pre-processing Photorealistic rendering (using Blender 2.68a) panoramic views both for probes and transition arcs We used 32 8-core PCs, for rendering times ranging from 40 minutes to 7 hours/model 1024^2 probe panoramas 256^2 transition video panoramas 59
  47. 47. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Explore Maps - Results 60
  48. 48. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Interactive Exploration UI for Explore Maps WebGL implementation + JPEG + MP4 Panoramic images: probes + transition path Closest probe selection Path alignment with current view Thumbnail goto Non-fixed orientation 61
  49. 49. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Conclusion: Interactive Exploration Interactive exploration of complex scenes Web/mobile enabled Pre-computed rendering state-of-the-art Global Illumination Graph-based navigation guided exploration Limitations Constrained navigation Fixed set of camera positions Limited interaction Exploit panoramic views on paths less constrained navigation 62
  50. 50. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Visualization techniques Applications Large meshes Scenes with complex lighting Volume rendering 69
  51. 51. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Practical Volume Rendering in Mobile Devices 70
  52. 52. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Our Goal 71
  53. 53. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Video 78 Low resolution when interacting Refine when still Samsung Galaxy S (512MB)
  54. 54. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Conclusions 2012 Volume rendering on mobile devices Possible but limited adaptive rendering (half resolution when interacting) 512MB RAM + 3x slice sets ~ 50-100MB was kind of limit for GPU resources 3D textures as GLES extension only on Qualcomm GPUs Nowadays 1-4 GB of RAM 3D textures in core GLES 3.0 1024^3 typically supported GPUs evolved -- complex shaders ?? ~2x-3x performance (~4fps ) 79
  55. 55. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Summary Interactive exploration of large mesh models Fixed coarse subdivision -- Adaptive QuadPatches Fixed number of patches, multiresolution inside patches Simple, but limited by topology issues Adaptive coarse subdivision -- Compact Adaptive TetraPuzzles Multiresolution by combining a variable number of fixed-size patches More general, but more complex Interactive exploration of complex scenes Guided navigation + Global Illumination, but limited freedom Practical volume rendering on mobile devices Almost there ? too much fragment load mobile GPU 90
  56. 56. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 SERVER MOBILE DEVICE Networked capture/rendering/interaction In the future... DATA ACCESS RENDERING DISPLAY INTERACTION Scene CAPTURE Environment NETWORK 91
  57. 57. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 SERVER MOBILE DEVICE Networked capture/rendering/interaction In the future... DATA ACCESS RENDERING DISPLAY INTERACTION Scene CAPTURE Environment NETWORK 92
  58. 58. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Opportunities Killer applications will be integration of Augmented reality / Mixed reality technology Big Data operations Real-time constraints Parallelization on a massive scale 93
  59. 59. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Challenges Input/Output: Augmented Reality headsets (Google glasses, pico projectors?) Environment imaging (acquisition and reconstruction) Computational: API improvements (HW progress faster than SW) Vulkan? Metal? Cloud-device integration Ultimate challenge: The network GPU Extend the GPU to network scale 109 GPUs for 1021 FLOPs ? Exciting era 94
  60. 60. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Questions and contacts CRS4 Visual Computing Group http://vic.crs4.it Speakers: Marco Agus [email protected] Marcos Balsa [email protected] The DIVA project http://diva-itn.ifi.uzh.ch This work is partially supported by the EU FP7 Program under the DIVA project (REA Agreement 290277). 95
  61. 61. Marco Agus & Marcos Balsa Visual Computing UniCa, June 17th, 2015 Questions and contacts CRS4 Visual Computing Group http://vic.crs4.it Speakers: Marco Agus [email protected] Marcos Balsa [email protected] The DIVA project http://diva-itn.ifi.uzh.ch This work is partially supported by the EU FP7 Program under the DIVA project (REA Agreement 290277). 96

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