transcript
- 1. www.crs4.it/vic/ Visual Computing 1: Scalable Graphics Marco
Agus Marcos Balsa Enrico Gobbetti Fabio Marton Jose Daz CRS4/ViC
June 2015
- 2. E. Gobbetti, F. Marton, Massive models, June 2015 CRS4
Center for Research, Development, and Advanced Studies in Sardinia
Interdisciplinary research center focused on computational sciences
Strong research program in CS + apps to IS / E&E / Bio Leading
computational + bioprocessing facilities Located in the POLARIS
Science and Technology Park (Pula, Sardinia, Italy) Operational
since 1992, RTD staff of ~130 people
- 3. E. Gobbetti, F. Marton, Massive models, June 2015 CRS4:
Visual Computing Group Main activities Visual Computing RTD 3D
scanning Staff Director: Enrico Gobbetti Main lab: Marco Agus,
Marcos Balsa, Fabio Bettio, Jose Daz, Fabio Marton, Gianni Pintore,
Ruggero Pintus, Antonio Zorcolo, Roberto Combet, Emilio Merella,
Alex Tinti Secretariat: Katia Brigaglia, Cinzia Sardu
- 4. E. Gobbetti, F. Marton, Massive models, June 2015 CRS4:
Visual Computing Group Externally funded research program Many EU
projects (4FP-7FP) and national projects Industrial projects from
GEXCEL, DIES, ENEL, ; Active presence in the scientific community
Many scientific collaborations: ISTI, UZH, JHU, HOL, KAIST, Over
150 refereed publications Conference organization Software products
and technology transfer Industrial software: point cloud
visualizers, surgical simulators, sciviz tools, Public software:
geoviewing, neuroimaging tools, Cultural Heritage Installations,
Educational Activities University courses; PhD students; Tutorials;
ITNs
- 5. E. Gobbetti, F. Marton, Massive models, June 2015 Research
RTD (mostly) connected to 3D massive models Many domains:
simulations, multimedia engineering, medical imaging, 3D scanning,
geospatial data, RTD covers acquisition, processing, distribution,
rendering, exploration plus other topics Simulators, 3DTV, 6
- 6. E. Gobbetti, F. Marton, Massive models, June 2015 Massive
research How to efficiently acquire/create massive models?
Computational photography, 3D scanning pipe-lines, 3DTV How to
efficiently process them? Stream-processing, multiresolution,
external memory algorithms, parallel programming, GPGPU Specific
processing methods How to efficiently store and distribute them?
Multiresolution, adaptive streaming, compression How to efficiently
render/interact with them? Multiresolution, adaptive
rendering/collisions, out-of-core methods, GPU programming,
parallelization, rasterization, ray-casting How to efficiently
explore them? Novel 3D displays, specific interaction techniques
Portable devices 7
- 7. E. Gobbetti, F. Marton, Massive models, June 2015 Effective
acquisition of color and shape of 3D models Combining acquired
colorimetric and geometric information using multiple sensors
acquiring 3D models in highly cluttered environments mapping
photographic datasets to dense 3D models acquired with laser
scanning capturing indoor scenes 8 SELECTED PUBLICATIONS: ACM JOCCH
2015 A Fast and Robust Framework for Semi-Automatic and Automatic
Registration of Photographs to 3D Geometry ACM JOCCH 2015 Mont'e
Scan: Effective Shape and Color Digitization of Cluttered 3D
Artworks C&G 2014 Automatic Room Detection and Reconstruction
in Cluttered Indoor Environments with Complex Room Layouts.
- 8. E. Gobbetti, F. Marton, Massive models, June 2015 Large
scale terrain mapping Parallel color/geometry fusion, compression,
Online regional systems Streaming reconstruction Streaming MLS
implementation Parallel, fast, scalable to gigabyte- sized models
Registration and blending Auto registration geometry to geometry
and photo to geometry Streaming seamless photo blending on massive
point clouds and triangle meshes Scalable surface processing 9
SELECTED PUBLICATIONS: ACM JOCCH 2015 A Fast and Robust Framework
for Semi-Automatic and Automatic Registration of Photographs to 3D
Geometry ACM JOCCH 2015 Mont'e Scan: Effective Shape and Color
Digitization of Cluttered 3D Artworks C&G 2014 Automatic Room
Detection and Reconstruction in Cluttered Indoor Environments with
Complex Room Layouts.
- 9. E. Gobbetti, F. Marton, Massive models, June 2015 Rendering
and streaming of terrains and urban environments Batched Dynamic
Adaptive Meshes First GPU-accelerated seamless variable resolution
methods for terrain rendering (*-BDAM) Compressed LODs for massive
urban models Blockmaps framework 10 SELECTED PUBLICATIONS: EG 2007:
Ray-Casted BlockMaps for Large Urban Models Visualization. EG 2006:
C-BDAM - Compressed Batched Dynamic Adaptive Meshes for Terrain
Rendering. EG 2003: BDAM - Batched Dynamic Adaptive Meshes for High
Performance Terrain Visualization. Second Best Paper Award.
- 10. E. Gobbetti, F. Marton, Massive models, June 2015
Processing, distribution, and rendering of massive dense 3D meshes
and point clouds Coarse grained multiresolution model based on
hierarchical volumetric decomposition First GPU bound technique for
massive meshes General framework based on an extension of the
Multi-Triangulation (GPU-MT) Optimized representation based on a
conformal hierarchy of tetrahedra Packed representation based on
quadrangulation (QuadPatches) Demonstrated on a number of test
cases, including all Digital Michelangelo and Monte Prama models 11
SELECTED PUBLICATIONS: IEEE VIS 2005, Batched Multi Triangulation.
In Proceedings IEEE Visualization C&G 2004, Layered Point
Clouds - a Simple and Efficient Multiresolution Structure for
Distributing and Rendering Gigantic Point-Sampled Models. SIGGRAPH
2004, Adaptive TetraPuzzles Efficient Out-of- core Construction and
Visualization of Gigantic Polygonal Models.
- 11. E. Gobbetti, F. Marton, Massive models, June 2015
Processing, distribution, and rendering of massive dense 3D meshes
and point clouds Coarse grained multiresolution model based on
hierarchical volumetric decomposition First GPU bound technique for
massive meshes General framework based on an extension of the
Multi-Triangulation (GPU-MT) Optimized representation based on a
conformal hierarchy of tetrahedra Packed representation based on
quadrangulation (QuadPatches) Demonstrated on a number of test
cases, including all Digital Michelangelo and Monte Prama models 12
SELECTED PUBLICATIONS: IEEE VIS 2005, Batched Multi Triangulation.
In Proceedings IEEE Visualization C&G 2004, Layered Point
Clouds - a Simple and Efficient Multiresolution Structure for
Distributing and Rendering Gigantic Point-Sampled Models. SIGGRAPH
2004, Adaptive TetraPuzzles Efficient Out-of- core Construction and
Visualization of Gigantic Polygonal Models.
- 12. E. Gobbetti, F. Marton, Massive models, June 2015
Processing, distribution, and rendering of huge complex 3D models
Visualization of very large arbitrary surface models
(CAD/simulation) based on volumetric multi-scale approximations Far
Voxels, Coherent Hierarchical Culling, Ray Tracing Far Voxels,
CHC+RT Image-based methods ExploreMaps, 13 SELECTED PUBLICATIONS:
Eurographics 2015 CHC+RT: Coherent Hierarchical Culling for Ray
Tracing Eurographics 2014: Efficient Construction and Ubiquitous
Exploration of Panoramic View Graphs of Complex 3D Environments
Siggraph 2005 Far Voxels - A Multiresolution Framework for
Interactive Rendering of Huge Complex 3D Models on Commodity
Graphics Platforms
- 13. E. Gobbetti, F. Marton, Massive models, June 2015 Massive
volumetric compression and rendering Render models of potentially
unlimited size on current GPU platforms. Methods based on adaptive
out-of-core multiresolution techniques visibility feedback
single-pass GPU raycasting framework Novel compression techniques
based on tensor decomposition sparse coding Fully interactive
performance on datasets of many GVoxels 14 SELECTED PUBLICATIONS:
CGF 2014 State-of-the-art in Compressed GPU-Based Direct Volume
Rendering. Eurovis 2012 COVRA: A compression-domain output-
sensitive volume rendering architecture based on a sparse
representation of voxel blocks. IEEE VIS 2011 Interactive
Multiscale Tensor Reconstruction for Multiresolution Volume
Visualization.
- 14. E. Gobbetti, F. Marton, Massive models, June 2015
Interactive visualization on remote, web, and mobile devices
Compact multiresolution mesh and point-cloud representations for
embedded platform and web scripting Asymmetric compression
framework, LODs, constrained techniques, image-based
representations ExploreMaps, Adaptive Quad Patches, Compressed
TetraPuzzles WebGL, Android, iOS 15 SELECTED PUBLICATIONS:
Eurographics 2014 ExploreMaps: Efficient Construction and
Ubiquitous Exploration of Panoramic View Graphs of Complex 3D Web3D
2013 Compression-domain Seamless Multiresolution Visualization of
Gigantic Meshes on Mobile Devices. Web3D 2012 Adaptive Quad
Patches: an Adaptive Regular Structure for Web Distribution and
Adaptive Rendering of 3D Models. Best Paper Award
- 15. E. Gobbetti, F. Marton, Massive models, June 2015 3D
multi-projector display with special holographic screen HW by
Holografika Objects appear floating in space Developed calibration
method, rendering systems, MCOP technique for rendering
Cluster-parallel visualization Special rendering techniques for
surfaces and volumes Illustrative methods on view- dependent
displays Special navigation techniques Maintain focus on displays
sweet spot Reduce DOFs to support casual users Parallel
multiresolution visualization on light field displays 16 SELECTED
PUBLICATIONS: The Visual Computer 2010 View-dependent Exploration
of Massive Volumetric Models on Large Scale Light Field Displays.
Eurographics 2008 GPU Accelerated Direct Volume Rendering on an
Interactive Light Field Display. C&G 2008 Scalable Rendering of
Massive Triangle Meshes on Light Field Displays.
- 16. E. Gobbetti, F. Marton, Massive models, June 2015 Novel
user interfaces for exploring 3D models Natural interfaces for
scene navigation and data exploration Assisted 3D scene exploration
Information presentation in scenes with annotations Exploration
tools for volumetric data Device-specific UIs (light- field
display, dual-display setups, mobile touch screens, ) 17 SELECTED
PUBLICATIONS: EuroVis 2015 Adaptive Recommendations for Enhanced
Non-linear Exploration of Annotated 3D Objects. JOCCH 2014 IsoCam:
Interactive Visual Exploration of Massive Cultural Heritage Models
on Large Projection Setups C&G 2012 Natural exploration of 3D
massive models on large-scale light field displays using the FOX
proximal navigation technique
- 17. E. Gobbetti, F. Marton, Massive models, June 2015 TODAYS
SEMINARS 18
- 18. E. Gobbetti, F. Marton, Massive models, June 2015 Todays
seminars 9:30 Enrico Gobbetti Opening 9:45 Fabio Marton Tecniche
per la visualizzazione in tempo reale di modelli 3D di grandi
dimensioni 11:00 Break 11:30 Marcos Balsa Marco Agus Mobile
Graphics: panoramica di applicazioni grafiche mobili e integrazione
con soluzioni multi-risoluzione 13:00 Break 14:30 Enrico Gobbetti
Fabio Marton State-of-the-art in Compressed GPU-Based Direct Volume
Rendering: part 1 Models and Preprocessing 16:00 Break 16:30 Jose
Daz Enrico Gobbetti State-of-the-art in Compressed GPU-Based Direct
Volume Rendering: part 2 Rendering 18:00 Questionario di
valutazione 19
- 19. E. Gobbetti, F. Marton, Massive models, June 2015 Follow-up
on 30/6/2015 dedicated to Cultural Heritage Computing 9:30 Enrico
Gobbetti Opening 9:45 Ruggero Pintus Gianni Pintore Shape Modeling
and acquisition 11:30 Break 12:00 Fabio Bettio Effective Shape and
Color Digitization of Cluttered 3D Artworks 12:30 Gianni Pintore
Simple Acquisition and Reconstruction of Multi- room Indoor
Structures 13:00 Break 14:30 Marcos Balsa Marco Agus Exploration of
Complex and Annotated 3D Models 16:00 Break 16:30 Enrico Gobbetti
Ruggero Pintus Geometric Analysis for Cultural Heritage 17:30
Questionario di valutazione 20
- 20. E. Gobbetti, F. Marton, Massive models, June 2015 AND MORE!
For more information: www.crs4.it/vic/
- 21. www.crs4.it/vic/ Massive models exploration: a short
overview Marco Agus Marcos Balsa Enrico Gobbetti Fabio Marton Jose
Daz CRS4/ViC June 2015
- 22. E. Gobbetti, F. Marton, Massive models, June 2015 MASSIVE
MODEL RENDERING A bit of context 23
- 23. E. Gobbetti, F. Marton, Massive models, June 2015 Context
and Motivation Explosion of data in all areas of science,
engineering, health and business applications, driven by
improvements in hardware and information processing technology
Acquisition: 3D imaging, remote sensing, range scanners, massive
picture collections, ubiquitous sensing devices, Computing:
modeling, simulations Need for novel tools, techniques, and
expertise! Our focus is 3D data Wide and deep impact on a variety
of domains 24
- 24. E. Gobbetti, F. Marton, Massive models, June 2015
Application domains / data sources Many important application
domains Todays models exceed O(108-1010) samples O(109-1011) bytes
Varying Dimensionality Topology Sampling distribution Terrain
Models 2.5D Flat/Spherical Dense regular sampling Urban models
2.5-3D Block structure Laser scanned models 3D Moderately simple
topology low depth complexity - dense CAD models 3D complex
topology high depth complexity structured - ugly mesh Natural
objects / Sim. results 3D complex topology + high depth complexity
+ unstructured/high frequency details Volumetric models 3D/4D
semitransparent volumes
- 25. E. Gobbetti, F. Marton, Massive models, June 2015 Massive
model rendering To explore massive 3D scenes we need to transform
them at interactive into a synthetic image that can be displayed on
the screen Two main families of algorithms Raytracing algorithms
Rasterization-based algorithms I/O Storage Screen 10-100 Hz
O(N=1M-100M) pixels O(K=unbounded) bytes (triangles, points, )
Limited bandwidth (network/disk/RAM/CPU/PCIe/GPU/) View parameters
Projection + Visibility + Shading
- 26. E. Gobbetti, F. Marton, Massive models, June 2015 Basic Ray
Tracing vs. Rasterization Rasterization Project scene to image
samples Ray Tracing Project image samples to scene For each image
pixel p: make a ray r For each scene primitive o: if intersect(r,o)
then find color for o color p with it For each scene primitive o:
find where o falls on screen rasterize 2D shape for each produced
pixel p: find color for o color p with it Lighting Projection
- 27. E. Gobbetti, F. Marton, Massive models, June 2015 Basic Ray
Tracing vs. Rasterization Rasterization Project scene to image
samples Ray Tracing Project image samples to scene For each image
pixel p: make a ray r For each scene primitive o: if intersect(r,o)
then find color for o color p with it For each scene primitive o:
find where o falls on screen rasterize 2D shape for each produced
pixel p: find color for o color p with it
- 28. E. Gobbetti, F. Marton, Massive models, June 2015
Scalability Traditional HPC, parallel rendering definitions Strong
scaling (more nodes are faster for same data) Weak scaling (more
nodes allow larger data) Our interest/definition: output
sensitivity Running time/storage proportional to size of output
instead of input Computational effort scales with visible data and
screen resolution Working set independent of original data size
29
- 29. E. Gobbetti, F. Marton, Massive models, June 2015 A
real-time data filtering problem! Models of unbounded complexity on
limited computers Need for output-sensitive techniques (O(N), not
O(K)) We assume less data on screen (N) than in model (K ) Need for
memory-efficient techniques (maximize cache hits!) Need for
parallel techniques (maximize CPU/GPU core usage) I/O Storage
Screen 10-100 Hz O(N=1M-100M) pixels O(K=unbounded) bytes
(triangles, points, ) Limited bandwidth
(network/disk/RAM/CPU/PCIe/GPU/) View parameters Projection +
Visibility + Shading
- 30. E. Gobbetti, F. Marton, Massive models, June 2015 A
real-time data filtering problem! Models of unbounded complexity on
limited computers Need for output-sensitive techniques (O(N), not
O(K)) We assume less data on screen (N) than in model (K ) Need for
memory-efficient techniques (maximize cache hits!) Need for
parallel techniques (maximize CPU/GPU core usage) I/O Storage
Screen 10-100 Hz O(N=1M-100M) pixels O(K=unbounded) bytes
(triangles, points, ) Limited bandwidth
(network/disk/RAM/CPU/PCIe/GPU/) View parameters Projection +
Visibility + Shading Small Working Set
- 31. E. Gobbetti, F. Marton, Massive models, June 2015
Output-sensitive techniques At preprocessing time: build MR
structure Data prefiltering! Visibility + simplification
Compression At run-time: selective view-dependent refinement from
out- of-core data Must be output sensitive Access to prefiltered
data under real-time constraints Visibility + LOD COARSE FINE
- 32. E. Gobbetti, F. Marton, Massive models, June 2015
Output-sensitive techniques At preprocessing time: build MR
structure Data prefiltering! Visibility + simplification
Compression At run-time: selective view-dependent refinement from
out- of-core/remote data Must be output sensitive Access to
prefiltered data under real-time constraints Decoding, Visibility +
LOD Occluded / Out-of-view Inaccurate Accurate FRONT
- 33. E. Gobbetti, F. Marton, Massive models, June 2015 Our
contributions: GPU-friendly output-sensitive techniques Chunk-based
multiresolution structures Amortize selection costs over groups of
primitives Combine space partitioning + level of detail Same
structure used for visibility and detail culling Seamless
combination of chunks Dependencies ensure consistency at the level
of chunks Complex rendering primitives GPU programming features
Curvilinear patches, view-dependent voxels, Chunk-based external
memory management Streaming, compression/decompression, block
transfers, caching
- 34. E. Gobbetti, F. Marton, Massive models, June 2015 35MPixel
displays 72 projectors 35 1GTri model on Light Field Displays
- 35. E. Gobbetti, F. Marton, Massive models, June 2015 and on
Mobile Terminals iPhone4 / iPad 36
- 36. E. Gobbetti, F. Marton, Massive models, June 2015 and we
can do volumes, too Direct Volume Rendering of 64GVoxel 37
- 37. E. Gobbetti, F. Marton, Massive models, June 2015 REAL-TIME
ADAPTIVE MESHES 38
- 38. E. Gobbetti, F. Marton, Massive models, June 2015 Real-time
adaptive meshes The problem: efficiently create view- dependent
meshes Constraints: must approximate original surface with
controlled screen-space error must preserve continuity (conforming
meshes) must handle meshes of varying topology must be efficiently
rendered
- 39. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
multiresolution structures Mix and match chunks Amortize CPU work!
Two approaches Adaptive coarse subdivision Multiresolution by
combining a variable number of fixed-size patches Chunked-MT
TetraPuzzles *-BDAM Fixed coarse subdivision Fixed number of
patches, multiresolution inside patches Adaptive QuadPatches
- 40. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
Multi Triangulations The Multi Triangulation Framework Theoretical
basis MT multiresolution framework (Puppo 1996) Our contribution
GPU friendly implementation based on surface chunks with boundary
constraints Optimized implicit specializations
(TetraPuzzles/V-Partitions) Parallel out-of-core pre- processing
and out-of-core run-time Partitioning and simplification Adaptive
rendering GPU Cache Multiresolution structure (data+dependency)
Off-line On-line Network / Bus References: EG 2003, 2006; IEEE Viz
2003, 2005; SIGGRAPH 2004; SPBG/C&G 2004; VAST 2009,2012; PG
2010,
- 41. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
Multi Triangulations The Multi Triangulation Framework Consider a
sequence of local modifications over a given description D Each
modification replaces a portion of the domain with a different
conforming portion (simplified) f1 floor / g1 the new fragment D=Df
g Di+1=Di gi+1
- 42. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
Multi Triangulations The Multi Triangulation Framework Dependencies
between modifications can be arranged in a DAG Adding a sink to the
DAG we can associate each fragment to an arc leaving a node
- 43. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
Multi Triangulations MT Cuts A cut of the DAG defines a new
representation Collect all the fragment floors of cut arcs and you
get a new conforming mesh D*=D0 g1 g4 = f0 f02 f03 f13 f1 f4
- 44. E. Gobbetti, F. Marton, Massive models, June 2015 Chunked
Multi Triangulations GPU Friendly MT Chunked MT assume fragments
are triangle patches with proper boundary constraints DAG
4Mtri/frame at >60 fps on modern GPUs Extremely high quality for
large dense models with well behaved surface
- 56. E. Gobbetti, F. Marton, Massive models, June 2015
Limitations of mesh-based multiresolution models Hard to apply to
models with high detail and complex topology and high depth
complexity! Error measured on boundary surfaces LOD construction
based on local surface coarsening/simplification operations LOD
construction unaware of visibility (view- independent
approximations)
- 57. E. Gobbetti, F. Marton, Massive models, June 2015 Sampled
representations First coarse-grained multiresolution point
hierarchy (LPC) Far voxels for Multi-scale modeling of appearance
rather than geometry, tight integration of visibility and LOD
construction Exploits GPU programmability for accelerated rendering
Many test cases, ranging from laser scans, to isosurfaces, to
extremely large CAD models Sampled representations C&G 2004,
SIGGRAPH 2005, VAST 2009, PG 2010, VC 2012, VAST 2012,
- 58. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels The Far Voxel Concept Assumption: opaque surfaces, non
participating medium Goal is to represent the appearance of complex
far geometry Near geometry can be represented at full resolution
Idea is to discretize a model into many small volumes located in
the neighborood of surfaces Approximates how a small subvolume of
the model reflects the incoming light => View-dependent cubical
voxel
- 59. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels The Far Voxel Concept Assumption: opaque surfaces, non
participating medium Goal is to represent the appearance of complex
far geometry Near geometry can be represented at full resolution
Idea is to discretize a model into many small volumes located in
the neighborhood of surfaces Approximates how a small subvolume of
the model reflects the incoming light => View-dependent
voxel
- 60. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels The Far Voxel Concept A far voxel returns color attenuation
given View direction Light direction Rendered using a customized
vertex shader executed on the GPU Shader = f (view direction, light
direction)
- 61. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview
- 62. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Inner nodes Sample a model subvolume
to build a grid of far voxels Voxels are far Project to worst case
max Viewed not closer than dmin D min Section of the 3D grid of far
voxels max
- 63. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Inner nodes Sample a model subvolume
to build a grid of far voxels Voxels are far Project to worst case
max Viewed not closer than dmin Raycasting samples original model
and identifies visible voxels D min Section of the 3D grid of far
voxels max
- 64. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Inner nodes Sample a model subvolume
to build a grid of far voxels Voxels are far Project to worst case
max Viewed not closer than dmin Raycasting samples original model
and identifies visible voxels D min Section of the 3D grid of far
voxels max
- 65. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Far Voxel Consider voxel subvolume
Samples gathered from unoccluded directions Sample: (BRDF, n) =
f(view direction)
- 66. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Far Voxel Consider voxel subvolume
Samples gathered from unoccluded directions Sample: (BRDF, n) =
f(view direction) Compress shading information by fitting samples
to a compact analytical representation
- 67. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Construction overview: Far Voxel Shaders Build all the K
different far voxels representations K = flat, smooth.. Principal
component analysis Evaluate each representation error Compare real
values (samples) with the voxel approximations from the sample
direction Choose approximation with lowest error Flat proxy: 2
components Smooth proxy: 6 components Others Err(k) =
- 68. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Rendering Hierarchical traversal with coherent culling Stop
when out-of view, occluded (GPU feedback), or accurate enough Leaf
node: Triangle rendering Draw the precomputed triangle strip Inner
node: Voxel rendering For each far voxel type Enable its shader
Draw all its view dependent primitives using glDrawArrays Splat
voxels as antialiased point primitives Limits Does not consider
primitive opacity Rendering quality similar to one-pass point splat
methods (no sorting/blending) Triangles Far Voxels
- 69. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Results Tested on extremely complex heterogeneous surface
models St.Matthew, Boeing 777, Richtmyer Meshkov isosurf., all at
once Tested in a number of situations Single processor / cluster
construction Workstation viewing, large scale display 373M
triangles 14.5 GB 350M triangles 13.7 GB 472M triangles 18.4 GB
1.2G triangles 46.6 GB
- 70. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Results Xeon 2.4GHz, 70GB SCSI 320 Disk, GeForce FX6800GT
AGP 8x (hardware dated back to 2005) Window size: from video
resolution to stereo projector display St.Matthew, Boeing,
Isosurface: 640 x 480 All at once: 640 x 480 and Stereo 2 x 1024 x
768 Pixel tolerance: [Target 1 | Actual ~0.9 | Max ~10] Resident
set size limited to ~200 MB 45 Fps 51 MPrim/s 44 Fps 42 MPrim/s 34
Fps 41 MPrim/s 2 x 1024 x 768 20 Fps 40 MPrim/s 640 x 480 20 Fps 42
MPrim/s
- 71. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels rendering of complex models. NVIDIA 6800 GTS (2005)
- 72. E. Gobbetti, F. Marton, Massive models, June 2015 Far
Voxels Conclusions General purpose technique that targets many
model kinds Seamless integration of multiresolution occlusion
culling out-of-core data management High performance Scalability
Main limitations Slow preprocessing Non-photorealistic rendering
quality Intel Xeon 2.4GHz 1GB, GeForce 6800GT AGP8X
- 73. E. Gobbetti, F. Marton, Massive models, June 2015 3D
VOLUMETRIC MODELS AND COMPRESSION 78
- 74. E. Gobbetti, F. Marton, Massive models, June 2015
Volumetric models Voxelized representation Regular grid structure
Simple scalar grid or sampled representation/voxel Data on surfaces
and/or interior of objects Advanced semitransp. shading
Increasingly common SciViz / Medical imaging Off-line rendering for
movies Gaming: voxel engines! Need for compression and efficient
rendering
- 75. E. Gobbetti, F. Marton, Massive models, June 2015
Visualization of massive scalar volumes without size limitations A
single-pass raycasting technique working out-of- core on GPU
parallel architectures Compress data to facilitate data streaming
and 4D visualizations Novel compression architecture and novel
compression methods Volumetric models References: EG 2008, Visual
Computer 2008, Visual Computer 2010, VG 2010, TVCG 2011, EUROVIS
2012 80
- 76. E. Gobbetti, F. Marton, Massive models, June 2015
Visualization of massive scalar volumes without size limitations A
single-pass raycasting technique working out-of- core on GPU
parallel architectures Compress data to facilitate data streaming
and 4D visualizations Novel compression architecture and novel
compression methods Volumetric models 81 Crassin - Gigavoxels
References: EG 2008, Visual Computer 2008, Visual Computer 2010, VG
2010, TVCG 2011, EUROVIS 2012 (STAR EG 2013)
- 77. E. Gobbetti, F. Marton, Massive models, June 2015 Order
dependentOrder independent Accumulation Empty space skipping Early
ray termination Pixel 82 Massive Volumes Visualization Volume
rendering problem
- 78. E. Gobbetti, F. Marton, Massive models, June 2015 Massive
Volumes Visualization Volume rendering problem Current interactive
solutions are based on GPU architectures Massive parallelism Huge
memory bandwidth E.g. GeForce GTX 780 Ti has a 336 GB/s of
bandwidth Has 5 GFLOPs [ hardwareinsight.com ] 83
- 79. E. Gobbetti, F. Marton, Massive models, June 2015 We
introduced a novel method based on the following basic ideas:
Multi-resolution out-of-core representation based on a octree of
volume bricks Adaptive CPU loading of the data from local/remote
repository cooperates with separate render thread fully executed in
the GPU Stackless traversal of an adaptive working set Exploitation
of the visibility feedback Massive Volumes Visualization
Multiresolution out-of-core DVR 84 SELECTED PUBLICATIONS: Real-time
deblocked GPU rendering of compressed volumes VMV , 2014.
View-dependent exploration of massive volumetric models on
large-scale light field displays. The Visual Computer, 26, 2010. A
single-pass GPU ray casting framework for interactive out-of-core
rendering of massive volumetric datasets. The Visual Computer, 24,
2008.
- 80. E. Gobbetti, F. Marton, Massive models, June 2015 An
adaptive cut of a multi- resolution octree structure is traversed
on the GPU, leading to a method which is scalable and fully
adaptive increases performance and reduces overhead produces simple
and flexible code (single-pass) Massive Volumes Visualization
Multiresolution out-of-core DVR 85
- 81. E. Gobbetti, F. Marton, Massive models, June 2015 Use CPU
for Creation & loading Octree refinement Encode current cut
using an spatial index Use GPU for Stackless octree traversal Using
neighbour pointers Rendering Flexible ray traversal / compositing
strategies Improved visibility feedback Massive Volumes
Visualization Multiresolution out-of-core DVR 86 Architecture
overview Neighbour pointer navigation
- 82. E. Gobbetti, F. Marton, Massive models, June 2015 volume
render adaptive loader storage preprocessing octree node database
visibility feedback has current working set enough accuracy? yes
octree refinement prepare to render no GPUCPU [ creation and
maintainance ] [ rendering ] offline Massive Volumes Visualization
Method overview 87
- 83. E. Gobbetti, F. Marton, Massive models, June 2015 The
adaptive loader maintains in-core a view-and- transfer function
dependent cut of the out-of-core octree structure Uses it to update
the GPU cache and Spatial Index. Uses CUDA scatter write capability
on a 8bit CUDA-array. Basic principles: Update at each frame the
visibility status of the nodes in the graph based on rendering
feedback Refine nodes marked as visible during the previous frame
and considered inaccurate and non-empty according to the current
transfer function Pull-up visibillity data to inner nodes by
recursively recombination The cost amortized over full brick
traversal is negligible (