Interactive View-Driven Evenly Spaced Streamline Placement Zhanping Liu Robert J. Moorhead II Visualization Analysis and Imaging Lab High Performance Computing.

Slides:



Advertisements
Similar presentations
Detection and Visualization of Defects in 3D Unstructured Models of Nematic Liquid Crystals Ketan Mehta* & T. J. Jankun-Kelly Viz Lab, Computer Science.
Advertisements

An Advanced Evenly-Spaced Streamline Placement Algorithm Zhanping Liu Robert J. Moorhead II Joe Groner Visualization Analysis and Imaging Lab High Performance.
Accelerating Real-Time Shading with Reverse Reprojection Caching Diego Nehab 1 Pedro V. Sander 2 Jason Lawrence 3 Natalya Tatarchuk 4 John R. Isidoro 4.
Disk Storage, Basic File Structures, and Hashing
Exploration of advanced lighting and shading techniques
An Optimized Soft Shadow Volume Algorithm with Real-Time Performance Ulf Assarsson 1, Michael Dougherty 2, Michael Mounier 2, and Tomas Akenine-Möller.
Image Repairing: Robust Image Synthesis by Adaptive ND Tensor Voting IEEE Computer Society Conference on Computer Vision and Pattern Recognition Jiaya.
Exploration of bump, parallax, relief and displacement mapping
CHAPTER 12 Height Maps, Hidden Surface Removal, Clipping and Level of Detail Algorithms © 2008 Cengage Learning EMEA.
Visibility Culling using Hierarchical Occlusion Maps Hansong Zhang, Dinesh Manocha, Tom Hudson, Kenneth E. Hoff III Presented by: Chris Wassenius.
Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes (CVPR’10) Shengcai Liao, Guoying Zhao, Vili Kellokumpu,
EE 7730 Image Segmentation.
EE663 Image Processing Edge Detection 5 Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
METU Department of Computer Eng Ceng 302 Introduction to DBMS Disk Storage, Basic File Structures, and Hashing by Pinar Senkul resources: mostly froom.
Memory Efficient Acceleration Structures and Techniques for CPU-based Volume Raycasting of Large Data S. Grimm, S. Bruckner, A. Kanitsar and E. Gröller.
Optimized Subdivisions for Preprocessed Visibility Oliver Mattausch, Jiří Bittner, Peter Wonka, Michael Wimmer Institute of Computer Graphics and Algorithms.
Haptic Cloth Rendering 6th Dutch-Belgian Haptics Meeting TUDelft, 21 st June 2006 Lode Vanacken Expertise centre for Digital Media (EDM) Hasselt University.
Tracking Video Objects in Cluttered Background
Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter 13 Disk Storage, Basic File Structures, and Hashing.
Spectral Processing of Point-sampled Geometry
Adaptive Global Visibility Sampling Jiří Bittner 1, Oliver Mattausch 2, Peter Wonka 3, Vlastimil Havran 1, Michael Wimmer 2 1 Czech Technical University.
The Story So Far The algorithms presented so far exploit: –Sparse sets of images (some data may not be available) –User help with correspondences (time.
Outline Reprojection and data reuse Reprojection and data reuse – Taxonomy Bidirectional reprojection Bidirectional reprojection.
Importance Driven Volume Rendering Authors: I. Viola, A. Kanitsar, M. Gröler Visualization II Instructor: Jessica Crouch.
Computer Graphics Mirror and Shadows
Modeling and representation 1 – comparative review and polygon mesh models 2.1 Introduction 2.2 Polygonal representation of three-dimensional objects 2.3.
Filtering Approaches for Real-Time Anti-Aliasing /
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 17 Disk Storage, Basic File Structures, and Hashing.
Surface Simplification Using Quadric Error Metrics Michael Garland Paul S. Heckbert.
Dobrina Boltcheva, Mariette Yvinec, Jean-Daniel Boissonnat INRIA – Sophia Antipolis, France 1. Initialization Use the.
Lei Zhang and Guoning Chen, Department of Computer Science, University of Houston Robert S. Laramee, Swansea University David Thompson and Adrian Sescu,
1 Speeding Up Ray Tracing Images from Virtual Light Field Project ©Slides Anthony Steed 1999 & Mel Slater 2004.
CDS 301 Fall, 2009 Vector Visualization Chap. 6 October 7, 2009 Jie Zhang Copyright ©
1 Rendering Geometry with Relief Textures L.Baboud X.Décoret ARTIS-GRAVIR/IMAG-INRIA.
Stream Processing Main References: “Comparing Reyes and OpenGL on a Stream Architecture”, 2002 “Polygon Rendering on a Stream Architecture”, 2000 Department.
Detail-Preserving Fluid Control N. Th ű rey R. Keiser M. Pauly U. R ű de SCA 2006.
Interactive Rendering With Coherent Ray Tracing Eurogaphics 2001 Wald, Slusallek, Benthin, Wagner Comp 238, UNC-CH, September 10, 2001 Joshua Stough.
Introduction to Level Set Methods: Part II
Lecture Fall 2001 From S. Zeki, Inner Vision.
Lei Zhang and Guoning Chen, Department of Computer Science, University of Houston Robert S. Laramee, Swansea University David Thompson and Adrian Sescu,
File Structures. 2 Chapter - Objectives Disk Storage Devices Files of Records Operations on Files Unordered Files Ordered Files Hashed Files Dynamic and.
3D Flow Visualization Xiaohong Ye
TEMPLATE DESIGN © A high-order accurate and monotonic advection scheme is used as a local interpolator to redistribute.
Duy & Piotr. How to reconstruct a high quality image with the least amount of samples per pixel the least amount of resources And preserving the image.
- Laboratoire d'InfoRmatique en Image et Systèmes d'information
Design and Implementation of Geometric and Texture-Based Flow Visualization Techniques Robert S. Laramee Markus Hadwiger Helwig Hauser.
H M V Amortized Supersampling Dec. 18, 2009, Pacifico Yokohama, Japan L EI Y ANG H, D IEGO N EHAB M, P EDRO V. S ANDER H, P ITCHAYA S ITTHI - AMORN V,
UNIT 5.  The related activities of sorting, searching and merging are central to many computer applications.  Sorting and merging provide us with a.
CS123 | INTRODUCTION TO COMPUTER GRAPHICS Andries van Dam © Visible Surface Determination (VSD) To render or not to render, that is the question… 1 of.
Pure Path Tracing: the Good and the Bad Path tracing concentrates on important paths only –Those that hit the eye –Those from bright emitters/reflectors.
Outline Introduction Research Project Findings / Results
Shadows David Luebke University of Virginia. Shadows An important visual cue, traditionally hard to do in real-time rendering Outline: –Notation –Planar.
01/28/09Dinesh Manocha, COMP770 Visibility Computations Visible Surface Determination Visibility Culling.
Hierarchical Occlusion Map Zhang et al SIGGRAPH 98.
Chapter 5 Record Storage and Primary File Organizations
DPL3/10/2016 CS 551/651: Simplification Continued David Luebke
1 Double-Patterning Aware DSA Template Guided Cut Redistribution for Advanced 1-D Gridded Designs Zhi-Wen Lin and Yao-Wen Chang National Taiwan University.
A novel approach to visualizing dark matter simulations
Computer Graphics Implementation II
Real-Time Soft Shadows with Adaptive Light Source Sampling
Pathology Spatial Analysis February 2017
Farthest Point Seeding for Efficient Placement of Streamlines
Visible-Surface Detection Methods
3D Object Representations
Implementation II Ed Angel Professor Emeritus of Computer Science
Visualization CSE 694L Roger Crawfis The Ohio State University.
RADEON™ 9700 Architecture and 3D Performance
Fourier Transform of Boundaries
An Advanced Evenly-Spaced Streamline Placement Algorithm
Interactive Sampling and Rendering for Complex and Procedural Geometry
Presentation transcript:

Interactive View-Driven Evenly Spaced Streamline Placement Zhanping Liu Robert J. Moorhead II Visualization Analysis and Imaging Lab High Performance Computing Collaboratory Mississippi State University IS & T / SPIE EI-VDA 2008

Outline  Results  Conclusions  Introduction  IVDESS  IVDESS Pipeline — physical-space streamline integration — view-space streamline density control  Temporally-Coherent Seeding Strategy (TCSS) vs. Temporally-Incoherent Seeding Strategy (TISS)  View-Sensitive Streamline Representation IS & T / SPIE EI-VDA 2008

Introduction IS & T / SPIE EI-VDA 2008 Texture-based (e.g., LIC) — powerful in visualizing 2D flows  Evenly Spaced Streamlines (ESS)  There have been many flow visualization methods Geometry-based (e.g., arrow plots)

Introduction  Evenly Spaced Streamlines (ESS) IS & T / SPIE EI-VDA 2008  Texture-based techniques may be ineffective for 2.5D/3D flows due to view occlusion, depth ambiguity, direction vagueness, & aliasing artifacts  Streamlines remain one of the most important 3D approaches for the straightforward direction cueing and the low computational expense

Introduction  Evenly Spaced Streamlines (ESS) IS & T / SPIE EI-VDA 2008  Without an effective placement strategy, streamlines tend to result in an incomplete coarse view or a global but cluttered image A heavily cluttered image may still miss an important flow feature (saddle here )

Introduction  Evenly Spaced Streamlines (ESS) IS & T / SPIE EI-VDA 2008  A layout of evenly spaced streamlines may provide an aesthetic & informative pattern to facilitate mental reconstruction of the flow here the saddle is clearly shown

Introduction  Evenly Spaced Streamlines (ESS)  To apply ESS to 3D exploration of volume flows, surface flows, & planar flows in a perspective-view setting, we need to address the foreshortening effect to obtain a visually uniform streamline placement — streamlines evenly spaced in 3D physical space (the flow field) may not visually retain the uniformity when projected to 2D view space (the output image) IS & T / SPIE EI-VDA 2008 the inter-frame transition to enable a temporally coherent flow exploration the practical applicability to provide an interactive grid-friendly solution O. Mattausch, T. Theubl, H. Hauser, and E. Groller Uniform in physical space but non-uniform in view space Streamlines that are evenly spaced in a 2D flow field are visually non-uniform in a perspective-view setting

Introduction  Existing ESS Algorithms  Image-guided methods  Sample-based methods Take a streamline placement as a binary-valued image Low-pass filter each intermediate placement and then compare it against a reference gray-scale image to guide iterative refinement toward an optimal Use inter-sample distance control to approximate inter-line distance control Distance checking is performed on each newly generated sample against other existing samples to determine if the distance is less than a threshold d IS & T / SPIE EI-VDA 2008

Introduction IS & T / SPIE EI-VDA 2008  ESS for Surface & Volume Flows  Physical-space ESS placement strategy multi-density representation — Mattausch et al [03] surface flows — Mao et al [98] volume flows — Ye et al [05]  View-space ESS placement strategy surface & volume flows — Li-Shen [07] Streamlines are indeed not evenly spaced in the output image

Introduction IS & T / SPIE EI-VDA 2008  IVDESS (Interactive View-Driven ESS)  built on ADVESS ( ADVanced ESS, Liu & Moorhead[06]) a 2D engine for sample-based streamline placement supports fast high-quality ESS placement with robust loop detection  for ESS -based 3D ( through perspective projection ) exploration of a planar flow a surface flow  essentially different from previous work in placing streamlines that are indeed evenly spaced in the output image providing a solution for coherent exploration of flows delivering high performance on a low-end PC

IVDESS IS & T / SPIE EI-VDA 2008  Basic Idea the non-uniform streamline placement of a planar flow in 3D physical space the resulting visually uniform layout in 2D view space (the output image) surface rendering depth acquisition streamline integration in physical space streamline-density control in view space whether a streamline is further advected or immediately terminated in physical space is governed by the status (accepted/rejected) of the newly generated point the projection of each streamline point and the associated view-space samples undergo inter-sample distance control to achieve inter-line distance control accept or reject point do point projection

IVDESS  The Pipeline IS & T / SPIE EI-VDA 2008  Dividing ADVESS Components into Two Spaces physical-space seeding is used to establish inter-frame coherence view-space seeding is used to create a separate frame of view-dependent evenly spaced streamlines each line segment is uniformly sampled in view space by thres. d inter-line distance control & intra-line distance control are both achieved using inter-sample distance control

IVDESS  The Pipeline IS & T / SPIE EI-VDA 2008  TISS ( T emporally I ncoherent S eeding S trategy ) — for separate frames  a view-space seeding scheme sort and insert Candidates introduced by the seed sample of a streamline are saved & sorted by the view-space streamline length in primary queue — a sorting queue Candidates introduced by each regular ( non-seed ) sample of a streamline are simply appended to the tail of secondary queue — a FIFO queue append to tail  adopts a double-queue seed scheduler primary queue head secondary queue head Primary queue takes priority over secondary queue in providing candidates Only when primary queue is temporarily empty is secondary queue used to either init the layout process or guarantee view coverage

IVDESS  Temporally Coherent Seeding Strategy IS & T / SPIE EI-VDA 2008  Building on top of TISS  IVDESS provides a multi-resolution ( in physical space ) flow representation and hence requires smooth inter-frame transition to achieve coherent flow exploration with visually uniform lines  TISS is an intra-frame view-space seeding mechanism without addressing explorative issues  IVDESS employs an inter-frame physical-space seeding scheme on top of TISS to constitute a  The inter-frame physical-space seeding scheme maintains temporal coherence by reusing and lengthening the streamlines of the previous frame under normal density control in the current frame Temporally Coherent Seeding Strategy ( TCSS ) — physical-space seeding prior to view-space seeding IVDESS  TCSS

IS & T / SPIE EI-VDA 2008  Efficient Greedy Non-split Streamline Reuse+Lengthening  Each streamline of the previous frame is accessed from physical- space storage and processed beginning with the seed in both directions — reprojection + resampling + possible lengthening  A streamline is potentially reused in either direction as long as the first in-view-segment sample passes inter-sample distance check greediness: a streamline with the seed out of the view may be reused otherwise: the disappearance of such streamlines brings big view change  A streamline is saved if it passes the view-space length check the accepted in-view part + the rejected in-view part + the out-of-view part

IVDESS  TCSS IS & T / SPIE EI-VDA 2008  Efficient Greedy Non-split Streamline Reuse+Lengthening  Point projection and segment sampling continue until any sample ( I 1 ) fails to pass inter-sample distance check  The first in-view segment sample ( I 0 ) in either direction is a raw segment sample — the projection of an in-view seed (S) an intermediate segment sample from line-view clipping (seed S out of view)

IS & T / SPIE EI-VDA 2008  C heck view-space length to decide if the streamline needs saving  Lengthening+projection+sampling occurs if the line end is reached I 0  R 0  R 1  I 1 ; I: Intermediate segment sample; R: Raw segment sample prevents the number of streamlines from excessively increasing suppresses incoherence / artifacts over the view boundaries  N on-split streamline reuse+lengthening IVDESS  TCSS  Efficient Greedy Non-split Streamline Reuse+Lengthening  Projection+sampling continues until any sample ( I 1 ) fails to pass inter-sample distance check  The first in-view segment sample ( I 0 ) in either direction is a raw segment sample — the projection of an in-view seed (S) an intermediate segment sample from line-view clipping (seed S out of view)  Lengthening+projection+sampling occurs if the line end is reached  Efficient ( projection + sample-in-view check  distance check in comp. cost ) allows closed streamlines to form Otherwise discontinuities would occur

IVDESS  View-Sensitive Streamline Representation IS & T / SPIE EI-VDA 2008  Complete Storage & Visibility Description  A streamline successfully reused in an IVDESS frame may include an out-of-view part and / or an in-view but rejected part while neither should be rendered to the output image  Physical-space raw points of a streamline are sequentially stored in the main body of a buffer from the negative end to the positive end number of raw points, seed’s buffer-index, view-space streamline length  Header of the streamline buffer  2 VSDs ( View-Sensitive Descriptors, one per direction ) after the header the first accepted in-view segment sample I 0 — 3 D coordinate the first accepted in-view raw point R 0 — buffer index the last accepted in-view raw point R 1 — buffer index the last accepted in-view segment sample I 1 — 3 D coordinate instantaneous adaptive step size closing point of a closed streamline  Lengthening+projection+sampling occurs if the line end is reached I 0  R 0  R 1  I 1 ; I: segment-view clip sample; R: Raw segment sample

The unprojection point of a line-view clip sample is temporarily stored in a VSD to render the current frame properly Otherwise jaggy lines might emerge as unintended unprojection points are stored in the main body and then used in the subsequent frames to lengthen streamlines  VSD s avoid jaggy lines resulting from unprojection errors  Thorough Reuse & Proper Rendering  VSDs provide a general description of the accepted viewable parts of a streamline to allow for greedy reuse+lengthening Redundancy may occur between fields and padding may be needed Fields need to be dynamically updated to keep track of the change IVDESS  View-Sensitive Streamline Representation IS & T / SPIE EI-VDA 2008 Jaggy lines emerge when unintended unprojection points (due to numerical error) of the current frame are reused in the subsequent frames to lengthen the streamlines

Results IS & T / SPIE EI-VDA 2008  Implementation & Test  Current implementation (using VC++ and OpenGL) — IVDESS for 3D exploration of planar flows in a perspective-view setting Notebook PC (Celeron M 1.60GHz/512MB RAM/Window XP/no GPU)  Test platform — a nowadays low-end facility  Test aspects — placement speed / placement quality / temporal coherence  Test dataset — a 468  337 2D flow field of the Northeast Pacific ocean  Perspective projection near clipping plane = 1.0 field-of-view angle = 90° far clipping plane = aspect ratio = 1.0 view size = 990  700  Initial step size (0.0625) & the adaptive range [10 -5, ] in cells  Threshold distance (10) & min streamline length (30) in pixels  100 IVDESS-TCSS (IVDESS) frames & 100 IVDESS-TISS frames were generated based on exactly the same exploration of the flow over one hundred critical points making a very complex flow pattern

Streamlines are evenly spaced in an IVDESS-TCSS frame without cluttering or distracting discontinuities. In particular, there are 3 closed streamlines successfully detected and formed.

The IVDESS-TCSS layout demonstrates the capability of our seeding strategy, even without topology-based seed distribution, in placing evenly spaced streamlines around critical points.

Results IS & T / SPIE EI-VDA 2008 Play the IVDESS-TISS movie! Play the IVDESS-TCSS movie!

number of streamlines per frame Results IS & T / SPIE EI-VDA 2008 This demonstrates the effectiveness of the streamline reuse+lengthening scheme of TCSS For more than half of the TCSS frames, there are far more reused streamlines than advected ones per frame. Even for the other frames, the number of reused streamlines is only a little bit less than that of advected ones per frame. TCSS-reused TCSS-advected TCSS total TISS total The total number of streamlines in a TCSS frame is very similar to that in a TISS frame. This indicates the high-performance of TCSS in preventing the number of streamlines from excessively increasing.

Results IS & T / SPIE EI-VDA 2008 streamline reuse percentage for each TCSS frame The high percentages demonstrate the effectiveness of the greedy non-split streamline reuse+lengthening scheme adopted in TCSS. streamlines obtained by reuse all streamlines in the previous frame percentage = streamlines obtained by reuse all streamlines in the current frame percentage =

time required per frame Results IS & T / SPIE EI-VDA 2008 The variation in frame generation time for TCSS is much less than that for TISS and this is also the case with frame generation+rendering time. For nearly every frame and for either case (generation time / generation+rendering time), less time was consumed by TCSS than by TISS. TCSS generation TCSS generation+rendering TISS generation TISS generation+rendering

frames per second Results IS & T / SPIE EI-VDA 2008 The interactive and nearly constant frame rates of TCSS indicate that IVDESS-TCSS (IVDESS) is well suited for coherent flow exploration. TCSS generation TCSS generation+rendering TISS generation TISS generation+rendering

Conclusions  IVDESS is a physically non-uniform but visually uniform representation of planar or curved surface flows in a perspective-view setting  IVDESS divides the view-dependent uniform placement process into physical-space flow integration & view-space streamline density control  A projection-unprojection pair is used via off-screening surface rendering to link the two spaces  Greedy but efficient non-split streamline reuse+lengthening is an inter-frame physical-space seeding scheme that is adopted on top of an intra-frame view-space seeding method to constitute a hybrid-space multi-level seeding mechanism — Temporally Coherent Seeding Strategy  A view-sensitive streamline representation is used to support thorough reuse+lengthening while guaranteeing proper rendering  IVDESS is well suited for coherent level-of-detail 3D exploration of large complex flows at interactive frame rates without either pre-processing or GPU support on a nowadays low-end PC IS & T / SPIE EI-VDA 2008

Conclusions IS & T / SPIE EI-VDA 2008  DoD HPCVI Program  Dr. David Kao  Anonymous reviewers  Acknowledgments  to enhance the current version of IVDESS in support of flows on curvilinear grids and unstructured grids  to investigate adaptive depth selection issues in an effort to extend IVDESS for explorative visualization of volume flows  Future Work IS & T / SPIE EI-VDA 2008 Thank you! Any questions?