Changeset 277 for trunk


Ignore:
Timestamp:
09/16/05 10:41:52 (19 years ago)
Author:
bittner
Message:

changes in the structure: renamed tools to algorithms

Location:
trunk/VUT/doc/SciReport
Files:
4 added
8 edited

Legend:

Unmodified
Added
Removed

  • trunk/VUT/doc/SciReport/Bib/jiri.bib

    r243 r277  
     1@Article{wimmer05:gpugems, 
     2  author =       {Michael Wimmer and Ji\v{r}\'\i{} Bittner}, 
     3  title =        {Hardware Occlusion Queries Made Useful}, 
     4  journal =      {GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation}, 
     5  year =         {2005}, 
     6  pages =        {91--108}, 
     7  publisher = {Addison Wesley} 
     8} 
     9 
    110@Article{bittner03:jep, 
    211  author =       {Ji\v{r}\'\i{} Bittner and Peter Wonka}, 
    … …  
    1221 
    1322 
    14 @PhdThesis{bittner:03:phd, 
     23@PhdThesis{bittner:02:phd, 
    1524  author =       {Ji\v{r}\'\i{} Bittner}, 
    1625  title =        {Hierarchical Techniques for Visibility Computations}, 

  • trunk/VUT/doc/SciReport/Bib/new.bib

    r251 r277  
     1@Article{avis96, 
     2  author = "David Avis and Komei Fukuda", 
     3  title = "Reverse Search for Enumeration", 
     4  journal =  "Discrete Applied Mathematics", 
     5  year = "1996", 
     6  volume = "6", 
     7  pages = "21--46" 
     8} 
     9 
     10@Article{Fukuda:1996:DDM, 
     11  author =       "K. Fukuda and A. Prodon", 
     12  title =        "Double Description Method Revisited", 
     13  journal =      "Lecture Notes in Computer Science", 
     14  volume =       "1120", 
     15  pages =        "91--111", 
     16  year =         "1996", 
     17  coden =        "LNCSD9", 
     18  ISSN =         "0302-9743", 
     19  bibdate =      "Mon Aug 25 16:23:54 MDT 1997", 
     20  acknowledgement = ack-nhfb, 
     21} 
     22 
     23@Misc{lrs_site, 
     24  author =       {David Avis}, 
     25  title  =       {LRS polyhedra enumeration library}, 
     26  year   =       {2002}, 
     27  note    =      "Available at \url{http://cgm.cs.mcgill.ca/~avis/C/lrs.html}", 
     28} 
     29 
     30@InProceedings{buehler2001, 
     31  pages =        "425--432", 
     32  year =         "2001", 
     33  title =        "Unstructured Lumigraph Rendering", 
     34  author =       "Chris Buehler and Michael Bosse and Leonard McMillan 
     35                 and Steven J. Gortler and Michael F. Cohen", 
     36  abstract =     "We describe an image based rendering approach that 
     37                 generalizes many current image based rendering 
     38                 algorithms, including light field rendering and 
     39                 view-dependent texture mapping. In particular, it 
     40                 allows for lumigraph-style rendering from a set of 
     41                 input cameras in arbitrary configurations (i.e., not 
     42                 restricted to a plane or to any specific manifold). In 
     43                 the case of regular and planar input camera positions, 
     44                 our algorithm reduces to a typical lumigraph approach. 
     45                 When presented with fewer cameras and good approximate 
     46                 geometry, our algorithm behaves like view-dependent 
     47                 texture mapping. The algorithm achieves this 
     48                 flexibility because it is designed to meet a set of 
     49                 specific goals that we describe. We demonstrate this 
     50                 flexibility with a variety of examples.", 
     51  keywords =     "Image-Based Rendering", 
     52  booktitle =    "Computer Graphics (SIGGRAPH '01 Proceedings)", 
     53} 
     54 
     55@InProceedings{haumont2005:egsr, 
     56  pages =        "211--222", 
     57  year =         "2005", 
     58  title =        "A Low Dimensional Framework for Exact Polygon-to-Polygon Occlusion Queries", 
     59  author =       {Denis Haumont and Otso M\"{a}kinen and Shaun Nirenstein}, 
     60  booktitle =    "Proceedings of Eurographics Symposium on Rendering", 
     61} 
     62 
     63 
     64 
     65@InProceedings{Sadagic, 
     66  pages =        "111--122", 
     67  year =         "2000", 
     68  title =        "Dynamic Polygon Visibility Ordering for Head-Slaved 
     69                 Viewing in Virtual Environments", 
     70  author =       "Amela Sadagic and Mel Slater", 
     71  url =          "http://visinfo.zib.de/EVlib/Show?EVL-2000-336", 
     72  abstract =     "This paper presents an approach to visibility called 
     73                 the Viewpoint Movement Space (VpMS) algorithm which 
     74                 supports the concept of dynamic polygon visibility 
     75                 orderings for head-slaved viewing in virtual 
     76                 environments (VE). The central idea of the approach is 
     77                 that the visibility, in terms of back-to-front polygon 
     78                 visibility ordering, does not change dramatically as 
     79                 the viewpoint moves. Moreover, it is possible to 
     80                 construct a partition of the space into cells, where 
     81                 for each cell the ordering is invariant. As the 
     82                 viewpoint moves across a cell boundary typically only a 
     83                 small and predictable change is made to the visibility 
     84                 ordering. The cost to perform this operation represents 
     85                 a notable reduction when compared with the cost of 
     86                 resolving the visibility information from the BSP tree 
     87                 where the classification of the viewpoint with every 
     88                 node plane has to be performed. The paper demonstrates 
     89                 how the subdivision into such cells can represent the 
     90                 basic source for an acceleration of the rendering 
     91                 process. We also discuss how the same supportive data 
     92                 structure can be exploited to solve other tasks in the 
     93                 graphics pipeline.", 
     94  organization = "Eurographics Association", 
     95  volume =       "19(2)", 
     96  booktitle =    "Computer Graphics Forum", 
     97} 
     98 
     99@Book{dcg-handbook, 
     100  title =        "Handbook of Discrete and Computational Geometry", 
     101  booktitle =    "Handbook of Discrete and Computational Geometry", 
     102  publisher =    "CRC Press", 
     103  year =         "1997", 
     104  editor =       "Jacob E. Goodman and Joseph O'Rourke", 
     105} 
     106 
     107@PhdThesis{Pu98-DSGIV, 
     108  author =       "Fan-Tao Pu", 
     109  year =         "1998", 
     110  title =        "Data Structures for Global Illumination and Visibility 
     111                 Queries in 3-Space", 
     112  address =      "College Park, MD", 
     113  school =       "University of Maryland", 
     114} 
     115 
    1116@TechReport{Hillesland02, 
    2117  author =       "Karl Hillesland and Brian Salomon and Anselmo Lastra 

  • trunk/VUT/doc/SciReport/analysis.tex

    r266 r277  
    11\chapter{Analysis of Visibility in Polygonal Scenes} 
    22 
     3\label{chap:analysis} 
     4 
    35This chapter provides analysis of the visibility in polygonal scenes, 
    4 which are the input for all developed tools. The visibility analysis 
     6which are the input for all developed algorithms. The visibility analysis 
    57uncoveres the complexity of the from-region and global visibility 
    68problems and thus it especially important for a good design of the 
    … …  
    911visibility interactions can be observed easily by interactions of sets 
    1012of points. Additionally for the sake of clarity, we first analyze 
    11 visibility in 2D and then extend the analysis to 3D polygonal scenes. 
     13visibility in 2D and then extend the analysis to 3D polygonal scenes. Visibility in 3D is described using \plucker  
    1214 
    1315\section{Analysis of visibility in 2D} 
    … …  
    5153coordinates $l^*_1 = (a, b, c)$ and $l^*_2 = -(a,b,c)$.  The \plucker 
    5254coordinates of 2D lines defined in this chapter are a simplified form 
    53 of the \plucker coordinates for 3D lines that will be discussed in 
    54 Chapter~\ref{chap:rot3d}: \plucker coordinates of a 2D line correspond 
     55of the \plucker coordinates for 3D lines, which will be discussed 
     56bellow in this chapter: \plucker coordinates of a 2D line correspond 
    5557to the \plucker coordinates of a 3D line embedded in the $z = 0$ plane 
    5658after removal of redundant coordinates (equal to 0) and permutation of 
    … …  
    287289primal space. This observation is supported by the classification of 
    288290visibility problems presented in 
    289 Chapter~\ref{chap:classes}. Visibility from point in 3D and visibility 
     291Chapter~\ref{chap:introduction}. Visibility from point in 3D and visibility 
    290292from region in 2D induce a two-dimensional problem-relevant line 
    291293set. This suggests the possibility of mapping one problem to another. 
    … …  
    500502equation~\ref{eq:plucker_quadric}. Thus there are four degrees of 
    501503freedom in the description of a 3D line, which conforms with the 
    502 classification from Chapter~\ref{chap:overview}. 
     504classification from Chapter~\ref{chap:introduction}. 
    503505 
    504506 \plucker coordinates allow to detect an incidence of two lines by 
    … …  
    799801 The blocker polyhedron describes lines intersecting the source 
    800802 polygon and the given occluder.  The blocker polyhedron can be seen 
    801  as an extension of the blocker polygon discussed in 
    802  Chapters~\ref{chap:rot2d} and~\ref{chap:rot3d} for the from-region 
     803 as an extension of the blocker polygon discussed above for the from-region 
    803804 visibility in 3D scenes. The blocker polyhedron is a 5D polyhedron in 
    804805 a 5D projective space.  To avoid singularities in the projection from 
    … …  
    899900 
    900901 
    901 For solution of some from-region visibility problems (e.g. PVS 
    902 computation, region-to-region visibility) the event swaths need not be 
    903 reconstructed. For example the visibility culling algorithm that will 
    904 be discussed in Section~\ref{sec:rot3pvs} only computes extremal 
    905 \plucker points and uses them to test an existence of a set of 
    906 stabbers of a given size. 
     902% For solution of some from-region visibility problems (e.g. PVS 
     903% computation, region-to-region visibility) the event swaths need not be 
     904% reconstructed. For example the visibility culling algorithm that will 
     905% be discussed in Section~\ref{sec:rot3pvs} only computes extremal 
     906% \plucker points and uses them to test an existence of a set of 
     907% stabbers of a given size. 
    907908 
    908909 

  • trunk/VUT/doc/SciReport/introduction.tex

    r273 r277  
    11\chapter{Introduction}%\chapter 
    22 
    3 \label{chap:overview} 
    4 \label{chap:classes} 
    5  
    6  This chapter provides introduction to visibility problems and 
    7 algorithms for computer graphics. In particular it presents a taxonomy 
    8 of visibility problems and algorithms. We discuss typical visibility 
    9 problems encountered in computer graphics including their relation to 
    10 the presented taxonomy. Finally, we classify the later described 
    11 visibility tools according to the presented taxonomy. 
    12  
     3\label{chap:introduction} 
    134 
    145\section{Structure of the report} 
     6 
     7  The report consists of two introductory chapters, which provide a 
     8 theoretical background for description of the algorithms, and three 
     9 chapters dealing with the actual visibility algorithms. 
     10 
     11  This chapter provides an introduction to visibility by using a 
     12 taxonomy of visibility problems and algorithms. The taxonomy is used 
     13 to classify the later described visibility 
     14 algorithms. Chapter~\ref{chap:analysis} provides an analysis of 
     15 visibility in 2D and 3D polygonal scenes. This analysis also includes 
     16 formal description of visibility using \plucker coordinates of 
     17 lines. \plucker coordinates are exploited later in algorithms for 
     18 mutual visibility verification (Chapter~\ref{chap:mutual}). 
     19 
     20 Chapter~\ref{chap:online} describes a visibility culling algorithm 
     21 used to implement the online visibility culling module. This 
     22 algorithm can be used accelerate rendering of fully dynamic scenes 
     23 using recent graphics hardware.  Chapter~\ref{chap:sampling} 
     24 describes global visibility sampling algorithm which forms a core of 
     25 the PVS computation module. This chapter also describes view space 
     26 partitioning algorithms used in close relation with the PVS 
     27 computation. Finally, Chapter~\ref{chap:mutual} describes mutual 
     28 visibility verification algorithms, which are used by the PVS 
     29 computation module to generate the final solution for precomputed 
     30 visibility. 
     31 
    1532 
    1633 
    … …  
    127144we obtain a vector that represents the same line $l$. More details 
    128145about this singularity-free mapping will be discussed in 
    129 Chapter~\ref{chap:vfr2d}. 
     146Chapter~\ref{chap:analysis}. 
    130147 
    131148 
    … …  
    207224 
    208225 Although the \plucker coordinates need more coefficients they have no 
    209 singularity and preserve some linearities: lines intersecting a set of  
    210 lines in 3D correspond to an intersection of 5D hyperplanes. More details 
    211 on \plucker coordinates will be discussed in 
    212 Chapters~\ref{chap:vfr25d} and~\ref{chap:vfr3d} where they are used to 
    213 solve the from-region visibility problem. 
     226singularity and preserve some linearities: lines intersecting a set of 
     227lines in 3D correspond to an intersection of 5D hyperplanes. More 
     228details on \plucker coordinates will be discussed in 
     229Chapter~\ref{chap:analysis} and Chapter~\ref{chap:mutual} where they 
     230are used to solve the from-region visibility problem. 
    214231 
    215232  To sum up: In 3D there are four degrees of freedom in the 
    … …  
    627644the nature of the given problem and it should assist in finding 
    628645relationships between visibility problems and algorithms in different 
    629 application areas.  The tools address the following classes of 
     646application areas.  The algorithms address the following classes of 
    630647visibility problems: 
    631648 
    … …  
    641658discussed important steps in the design of a visibility algorithm that 
    642659should also assist in understanding the quality of a visibility 
    643 algorithm. According to the classification the tools address 
    644 algorithms with the following properties: 
     660algorithm. According to the classification the visibility algorithms 
     661described later in the report address algorithms with the following 
     662properties: 
    645663 
    646664\begin{itemize} 
    647665\item Domain: 
    648666  \begin{itemize} 
    649   \item viewpoint (online culling tool), 
    650   \item global visibility (global visibility sampling tool) 
    651   \item polygon or polyhedron (mutual visibility tool) 
     667  \item viewpoint (online visibility culling), 
     668  \item global visibility (global visibility sampling) 
     669  \item polygon or polyhedron (mutual visibility verification) 
    652670  \end{itemize} 
    653671\item Scene restrictions (occluders): 
    … …  
    671689\item Solution space: 
    672690  \begin{itemize} 
    673   \item discrete (online culling tool, global visibility sampling tool, conservative and approximate algorithm from the mutual visibility tool) 
    674   \item continuous (exact algorithm from mutual visibility tool) 
     691  \item discrete (online visibility culling, global visibility sampling, conservative and approximate algorithm from the mutual visibility verification) 
     692  \item continuous (exact algorithm from mutual visibility verification) 
    675693  \end{itemize} 
    676   \item Solution space data structures: viewport (online culling tool), ray stack (global visibility sampling tool, conservative and approximate algorithm from the mutual visibility tool), BSP tree (exact algorithm from the mutual visibility tool) 
     694  \item Solution space data structures: viewport (online visibility culling), ray stack (global visibility sampling, conservative and approximate algorithm from the mutual visibility verification), BSP tree (exact algorithm from the mutual visibility verification) 
    677695  \item Use of coherence of visibility: 
    678696    \begin{itemize} 
    679     \item spatial coherence (all tools) 
    680     \item temporal coherence (online culling tool) 
     697    \item spatial coherence (all algorithms) 
     698    \item temporal coherence (online visibility culling) 
    681699    \end{itemize} 
    682   \item Output sensitivity: expected in practice (all tools) 
    683   \item Acceleration data structure: kD-tree (all tools) 
    684   \item Use of graphics hardware: online culling tool 
     700  \item Output sensitivity: expected in practice (all algorithms) 
     701  \item Acceleration data structure: kD-tree (all algorithms) 
     702  \item Use of graphics hardware: online visibility culling 
    685703  \end{itemize} 
    686704 

  • trunk/VUT/doc/SciReport/mutual.tex

    r273 r277  
    1 \chapter{Mutual Visibility Tool} 
    2  
    3  
    4 If a fast visibility solution is required such as during the 
    5 development cycle then the aggresive visibility sampling discussed in 
    6 the previous chapter is a good candidate. However for final solution 
    7 (production) we should either be sure that there can be no visible 
    8 error due to the preprocessed visibility or the error is suffieciently 
    9 small and thus not noticable. 
    10  
    11 The verification starts with identifiying a minimal set of object/view 
    12 cell pairs which are classified as mutually invisible. We process the 
    13 objects sequentially and for every given object we determine the view 
    14 cells which form the boundary of the invisible view cell set. 
     1\chapter{Mutual Visibility Verification} 
     2 
     3\label{chap:mutual} 
     4 
     5The aggressive visibility sampling discussed in the previous chapter is 
     6a good candidate if a fast visibility solution is required (e.g. 
     7during the development cycle). However for final solution (production) 
     8we should either be sure that there can be no visible error due to the 
     9preprocessed visibility or the error is sufficiently small and thus 
     10not noticeable. 
     11 
     12The mutual visibility verification starts with identifying a minimal 
     13set of object/view cell pairs which are classified as mutually 
     14invisible. We process the objects sequentially and for every given 
     15object we determine the view cells which form the boundary of the 
     16invisible view cell set. 
    1517 
    1618This boundary it is based on the current view cell visibility 
    … …  
    3537Below, we describe three different mutual visibility verification 
    3638algorithms. The first algorithm which is described in most detail 
    37 computes exact visibility. The second one is a coservative algorithm 
     39computes exact visibility. The second one is a conservative algorithm 
    3840and the third one is an approximate algorithm with a guaranteed error 
    3941bound. 
    … …  
    5355 
    5456 The occlusion tree for the visibility from region problem is a 5D BSP 
    55 tree maintaining a collection of the 5D blocker polyhedra. The tree is 
    56 constructed for each source polygon $P_S$ and represents all rays 
    57 emerging from $P_S$. Each node $N$ of the tree represents a subset of 
    58 line space ${\mathcal Q^*_N}$. The root of the tree represents the 
    59 whole problem-relevant line set ${\mathcal L}_R$. If $N$ is an 
    60 interior node, it is associated with a \plucker plane $\pplane_N$. 
    61 Left child of $N$ represents ${\mathcal Q^*_N} \cap \pplane^-_N$, 
    62 right child ${\mathcal Q^*_N} \cap \pplane^+_N$, where $\pplane^-_N$ 
    63 and $\pplane^+_N$ are halfspaces induced by $\pplane_N$. 
     57tree maintaining a collection of the 5D blocker 
     58polyhedra~\cite{bittner:02:phd}. The tree is constructed for each 
     59source polygon $P_S$ and represents all rays emerging from $P_S$. Each 
     60node $N$ of the tree represents a subset of line space ${\mathcal 
     61Q^*_N}$. The root of the tree represents the whole problem-relevant 
     62line set ${\mathcal L}_R$. If $N$ is an interior node, it is 
     63associated with a \plucker plane $\pplane_N$.  Left child of $N$ 
     64represents ${\mathcal Q^*_N} \cap \pplane^-_N$, right child ${\mathcal 
     65Q^*_N} \cap \pplane^+_N$, where $\pplane^-_N$ and $\pplane^+_N$ are 
     66halfspaces induced by $\pplane_N$. 
    6467 
    6568 Leaves of the occlusion tree are classified $in$ or $out$. If $N$ is 
    … …  
    7982are represented by polyhedra P1, P2 and P3. The occlusion tree represents a union 
    8083of the polyhedra. Note that P2 has been split during the insertion process.} 
    81 \label{fig:duality2d} 
     84\label{fig:ot} 
    8285\end{figure} 
    8386 
    … …  
    200203 
    201204 
    202 \section{Visibility test} 
     205\subsection{Visibility test} 
    203206 
    204207\label{sec:rot3d_vistest} 
    … …  
    216219tested for visibility by the traversal of the occlusion tree. The 
    217220visibility test proceeds as the algorithms described in 
    218 Section~\ref{sec:rot3d_construct}, but no modifications to the tree are 
    219 performed. If the polyhedron is classified as visible in all reached 
    220 leaves, the current face is fully visible. If the polyhedron is 
    221 invisible in all reached leaves, the face is invisible. Otherwise it 
    222 is partially visible since some rays connecting the face and the 
     221Section~\ref{sec:rot3d_construct}, but no modifications to the tree 
     222are performed. If the polyhedron is classified as visible in all 
     223reached leaves, the current face is fully visible. If the polyhedron 
     224is invisible in all reached leaves, the face is invisible. Otherwise 
     225it is partially visible since some rays connecting the face and the 
    223226source polygon are occluded and some are unoccluded. The visibility of 
    224 the whole region is computed using a combination of visibility states 
    225 of its boundary faces according to Table~\ref{table:vis_states}. 
     227the whole region can computed using a combination of visibility states 
     228of its boundary faces according to 
     229Table~\ref{table:vis_states}. However, since we are interested only in 
     230verification of mutual invisibility as soon as we find a first visible 
     231fragment the test can be terminated. 
     232 
     233\begin{table}[htb] 
     234\begin{center} 
     235\begin{tabular}{|c|c||c|} 
     236\hline 
     237Fragment A & Fragment B & A $\cup$ B \\ 
     238\hline 
     239\hline 
     240F & F & F\\ 
     241\hline 
     242I & I & I\\ 
     243\hline 
     244I & F & P\\ 
     245\hline 
     246F & I & P\\ 
     247\hline 
     248P & $*$ & P\\ 
     249\hline 
     250$*$ & P & P\\  
     251\hline 
     252\end{tabular} 
     253\begin{tabular}{cl} 
     254I  & -- invisible\\ 
     255P    & -- partially visible\\ 
     256F  & -- fully visible\\ 
     257$*$  & -- any of the I,P,F states 
     258\end{tabular} 
     259\end{center} 
     260\caption{Combining visibility states of two fragments.} 
     261\label{table:vis_states} 
     262\end{table} 
    226263 
    227264 
    … …  
    342379visibility verifier described above. The verifier is an extension of 
    343380the strong occlusion algorithm of Cohen-Or et 
    344 al.~\cite{cohen-egc-98}. In particular our verfier refines the search 
     381al.~\cite{cohen-egc-98}. In particular our verifier refines the search 
    345382for a strong occluder by using a hierarchical subdivision of space of 
    346383lines connecting the two regions tested for mutual 
    … …  
    359396\item If the rays do not intersect a single convex object four new 
    360397shafts are constructed by splitting both regions in half and the 
    361 process is repeated recursivelly. 
     398process is repeated recursively. 
    362399\end{itemize} 
    363400 
    364401If the subdivision does not terminate till reaching a predefined 
    365 subdivision depth, we conservativelly classify the tested regions as 
     402subdivision depth, we conservatively classify the tested regions as 
    366403mutually visible. 
    367404 
    … …  
    425462\label{sec:rot3d_summary} 
    426463 
    427  This chapter presented a mutual visibility tool, which determines 
     464 This chapter presented a mutual visibility verification algorithms, which determine 
    428465 whether two regions in the scene are visible. 
    429466 
    430  First, we have described an advanced exact visibility algorithm which 
    431 is a simple of modification of an algorithm for computing from-region 
     467 First, we have described an exact visibility algorithm which is a 
     468simple of modification of an algorithm for computing from-region 
    432469visibility in polygonal scenes. The key idea is a hierarchical 
    433470subdivision of the problem-relevant line set using \plucker 
    … …  
    449486Second, we have described a conservative verifier which is an 
    450487extension of the algorithm based on strong occluders. The conservative 
    451 verifier is requires a specification of the shaft size at which the 
    452 tested regions are conservativelly classfied as visible. 
    453  
    454 Third, we have described an approximate error bound verifier which is 
    455 an extension of the algorithm based on strong occluders. The 
    456 conservative verifier is requires a specification of the shaft size at 
    457 which the tested regions are conservativelly classfied as visible. 
    458  
     488verifier requires a specification of the shaft size at which the 
     489tested regions are conservatively classified as visible. 
     490 
     491Third, we have described an approximate error bound verifier which 
     492extends the conservative verifier by using automatic termination 
     493criteria based on the estimation of maximal error for the given shaft. 
     494 

  • trunk/VUT/doc/SciReport/online.tex

    r273 r277  
    1 \chapter{Online Culling Tool} 
     1\chapter{Online Visibility Culling} 
    22%***************************************************************************** 
     3\label{chap:online} 
    34 
    45\section{Introduction} 
    … …  
    7475simplicity and versatility: the method can be easily integrated in 
    7576existing real-time rendering packages on architectures supporting the 
    76 underlying occlusion query.  In 
     77underlying occlusion query~\cite{wimmer05:gpugems}.  In 
    7778figure~\ref{fig:online_culling_example}, the same scene (top row) is 
    7879rendered using view frustum culling (visualization in the bottom left 
    79 image) versus online culling using occlusion queries (visualization 
    80 in the bottom right image). It can be seen that with view frustum 
    81 culling only many objects are still rendered. 
     80image) versus online culling using occlusion queries (visualization in 
     81the bottom right image). It can be seen that with view frustum culling 
     82only many objects are still rendered. 
    8283%Using spatial and assuming temporal coherence 
    8384 
    … …  
    177178%% occlusion trees~\cite{bittner98b_long}. 
    178179 
    179 On a theoretical level, our paper is related to methods aiming to 
     180On a theoretical level, our method is related to methods aiming to 
    180181exploit the temporal coherence of visibility. Greene et 
    181182al.~\cite{Greene93a} used the set of visible objects from one frame to 
    … …  
    194195acceleration technique for exploiting spatial and temporal coherence 
    195196in hierarchical visibility algorithms. The central idea, which is also 
    196 vital for the online culling tool, is to avoid repeated visibility tests of 
    197 interior nodes of the hierarchy. The problem of direct adoption of 
    198 this method is that it is designed for the use with instantaneous CPU 
    199 based occlusion queries, whereas hardware occlusion queries exhibit 
    200 significant latency. The method presented herein efficiently overcomes 
    201 the problem of latency while keeping the benefits of a generality and 
    202 simplicity of the original hierarchical technique. As a result we 
    203 obtain a simple and efficient occlusion culling algorithm utilizing 
    204 hardware occlusion queries. 
     197vital for the online occlusion culling, is to avoid repeated 
     198visibility tests of interior nodes of the hierarchy. The problem of 
     199direct adoption of this method is that it is designed for the use with 
     200instantaneous CPU based occlusion queries, whereas hardware occlusion 
     201queries exhibit significant latency. The method presented herein 
     202efficiently overcomes the problem of latency while keeping the 
     203benefits of a generality and simplicity of the original hierarchical 
     204technique. As a result we obtain a simple and efficient occlusion 
     205culling algorithm utilizing hardware occlusion queries. 
    205206 
    206207 
    … …  
    209210 
    210211 
    211 The rest of the paper is organized as follows: 
    212 Section~\ref{sec:hwqueries} discusses hardware supported occlusion 
    213 queries and a basic application of these queries using a kD-tree. 
    214 Section~\ref{sec:hoq} presents our new algorithm and 
    215 Section~\ref{sec:optimizations} describes several additional 
    216 optimizations. Section~\ref{sec:results} presents results obtained by 
    217 experimental evaluation of the method and discusses its 
    218 behavior. Finally Section~\ref{sec:conclusion} concludes the paper. 
     212% The rest of the chapter is organized as follows: 
     213% Section~\ref{sec:hwqueries} discusses hardware supported occlusion 
     214% queries and a basic application of these queries using a kD-tree. 
     215% Section~\ref{sec:hoq} presents our new algorithm and 
     216% Section~\ref{sec:optimizations} describes several additional 
     217% optimizations. Section~\ref{sec:results} presents results obtained by 
     218% experimental evaluation of the method and discusses its 
     219% behavior. Finally Section~\ref{sec:conclusion} concludes the chapter. 
    219220 
    220221 
    … …  
    11191120\begin{figure*}[htb] 
    11201121  \centerline{ 
    1121      \includegraphics[width=0.4\textwidth,draft=\DRAFTFIGS]{images/teapots} 
    1122      } 
     1122    \hfill 
     1123    \includegraphics[height=0.3\textwidth,draft=\DRAFTFIGS]{images/teapots} 
     1124    \hfill 
     1125    \includegraphics[height=0.3\textwidth,draft=\DRAFTFIGS]{images/city} 
     1126    \hfill 
     1127  } 
    11231128  \centerline{ 
    1124      \includegraphics[width=0.4\textwidth,draft=\DRAFTFIGS]{images/city} 
    1125      } 
    1126   \centerline{ 
    1127      \includegraphics[width=0.4\textwidth,draft=\DRAFTFIGS]{images/pplant} 
    1128      } 
    1129     
    1130    \caption{The test scenes: the teapots, the city, and the power plant.} 
    1131    \label{fig:scenes} 
     1129    \includegraphics[width=0.35\textwidth,draft=\DRAFTFIGS]{images/pplant} 
     1130  } 
     1131   
     1132  \caption{The test scenes: the teapots, the city, and the power plant.} 
     1133  \label{fig:scenes} 
    11321134\end{figure*} 
    11331135 

  • trunk/VUT/doc/SciReport/report.tex

    r273 r277  
    88\usepackage{times} 
    99\usepackage{a4wide} 
     10\usepackage{pdfpages} 
     11\usepackage{float} 
    1012 
    1113% For backwards compatibility to old LaTeX type font selection. 
    … …  
    128130 
    129131 
     132\makeindex 
    130133 
    131134%------------------------------------------------------------------------- 
    132135\begin{document} 
    133  
     136\setcounter{tocdepth}{1} 
     137\includepdf[pages=1-2]{cover.pdf} 
     138\pagenumbering{roman} 
    134139\tableofcontents 
     140\pagenumbering{arabic} 
     141 
    135142 
    136143% --------------------------------------------------------------------- 
    … …  
    162169 
    163170 
    164 \maketitle 
     171%\maketitle 
    165172 
    166173\include{introduction} 

  • trunk/VUT/doc/SciReport/sampling.tex

    r273 r277  
    1 \chapter{Global Visibility Sampling Tool} 
    2  
     1\chapter{Global Visibility Sampling} 
     2 
     3\label{chap:sampling} 
    34 
    45The proposed visibility preprocessing framework consists of two major 
    … …  
    67 
    78\begin{itemize} 
    8 \item The first step is an aggresive visibility sampling which gives 
     9\item The first step is an aggressive visibility sampling which gives 
    910initial estimate about global visibility in the scene. The sampling 
    1011itself involves several strategies which will be described bellow. 
    11 The imporant property of the aggresive sampling step is that it 
     12The important property of the aggressive sampling step is that it 
    1213provides a fast progressive solution to global visibility and thus it 
    1314can be easily integrated into the game development cycle. The 
    14 aggresive sampling will terminate when the average contribution of new 
     15aggressive sampling will terminate when the average contribution of new 
    1516ray samples falls bellow a predefined threshold. 
    1617 
    1718\item The second step is mutual visibility verification. This step 
    18 turns the previous aggresive visibility solution into either exact, 
    19 conservative or error bound aggresive solution. The choice of the 
     19turns the previous aggressive visibility solution into either exact, 
     20conservative or error bound aggressive solution. The choice of the 
    2021particular verifier is left on the user in order to select the best 
    2122one for a particular scene, application context and time 
    2223constrains. For example, in scenes like a forest an error bound 
    23 aggresive visibility can be the best compromise between the resulting 
    24 size of the PVS (and framerate) and the visual quality. The exact or 
     24aggressive visibility can be the best compromise between the resulting 
     25size of the PVS (and frame rate) and the visual quality. The exact or 
    2526conservative algorithm can however be chosen for urban scenes where 
    26 ommision of even small objects can be more distructing for the 
    27 user. The mutual visibility tool will be described in the next chapter. 
     27omission of even small objects can be more distracting for the 
     28user. The mutual visibility verification will be described in the next 
     29chapter. 
    2830 
    2931\end{itemize} 
    … …  
    3133In traditional visibility preprocessing the view space is 
    3234subdivided into view cells and for each view cell the set of visible 
    33 objects --- potentially visible set (PVS) is computed. This framewoirk 
    34 has been used for conservative, aggresive and exact algorithms. 
     35objects --- potentially visible set (PVS) is computed. This framework 
     36has been used for conservative, aggressive and exact algorithms. 
    3537 
    3638We propose a different strategy which has several advantages for 
    37 sampling based aggresive visibility preprocessing. The stategy is 
     39sampling based aggressive visibility preprocessing. The strategy is 
    3840based on the following fundamental ideas: 
    3941\begin{itemize} 
    … …  
    6365the partitioning. The major drawback of this approach is that for 
    6466polygonal scenes with $n$ polygons there can be $\Theta(n^9)$ cells in 
    65 the partitioning for unrestricted viewspace. A {\em scale space} 
     67the partitioning for unrestricted view space. A {\em scale space} 
    6668aspect graph~\cite{bb12595,bb12590} improves robustness of the method 
    6769by merging similar features according to the given scale. 
    … …  
    121123The exact mutual visibility method presented later in the report is 
    122124based on method exploting \plucker coordinates of 
    123 lines~\cite{bittner02phd,nirenstein:02:egwr,haumont2005}. This 
     125lines~\cite{bittner:02:phd,nirenstein:02:egwr,haumont2005:egsr}. This 
    124126algorithm uses \plucker coordinates to compute visibility in shafts 
    125127defined by each polygon in the scene. 
    … …  
    242244 
    243245 
    244 \begin{figure}[htb] 
    245   \centerline{ 
    246     \includegraphics[height=0.35\textwidth,draft=\DRAFTFIGS]{images/vienna_viewcells_01} 
    247      \includegraphics[height=0.35\textwidth,draft=\DRAFTFIGS]{images/vienna_viewcells_07} 
    248     } 
    249  
    250   \caption{(left) Vienna viewcells. (right) The view cells are prisms with a triangular base. } 
    251   \label{fig:vienna_viewcells} 
    252 \end{figure} 
    253246 
    254247 
    … …  
    257250partition of the scene into view cells is an essential part of every 
    258251visibility system. If they are chosen too large, the PVS (Potentially 
    259 Visibible Set)  of a view cells is too large for efficient culling. If 
     252Visible Set)  of a view cells is too large for efficient culling. If 
    260253they are chosen too small or without consideration, then neighbouring 
    261254view cells contain redundant PVS information, hence boosting the PVS 
    … …  
    266259height to form a view cell prism. 
    267260 
     261\begin{figure}[htb] 
     262  \centerline{ 
     263    \includegraphics[height=0.35\textwidth,draft=\DRAFTFIGS]{images/vienna_viewcells_01} 
     264     \includegraphics[height=0.35\textwidth,draft=\DRAFTFIGS]{images/vienna_viewcells_07} 
     265    } 
     266 
     267  \caption{(left) Vienna view cells. (right) The view cells are prisms with a triangular base. } 
     268  \label{fig:vienna_viewcells} 
     269\end{figure} 
     270 
    268271In order to efficiently use view cells with our sampling method, we 
    269272require a view cell representation which is 
    … …  
    275278\end{itemize} 
    276279 
    277 We meet these requirements by employing spatial subdivisions (i.e.,  
    278 KD trees and BSP trees), to store the view cells. The initial view cells are 
    279 associated with the leaves. The reason why we chose BSP trees as view cell representation  
    280 is that they are very flexible. View cells forming arbitrary closed meshes can be closely matched. 
    281 Therefore we are able to find a view cells with only a few view ray-plane 
    282 intersections.  Furthermore, the hierarchical structures can be 
    283 exploited as hierarchy of view cells. Interior nodes form larger view cells 
    284 containing the children. If neccessary, a leaf can be easily subdivided into smaller view cells. 
    285  
    286 Currently we consider three different approaches to generate the initial view cell BSP tree. 
    287 The third method is not restricted to BSP trees, but BSP trees are prefered  
    288 because of their greater flexibility. 
     280We meet these requirements by employing spatial subdivisions (i.e., 
     281KD trees and BSP trees), to store the view cells. The initial view 
     282cells are associated with the leaves. The reason why we chose BSP 
     283trees as view cell representation  is that they are very 
     284flexible. View cells forming arbitrary closed meshes can be closely 
     285matched.  Therefore we are able to find a view cells with only a few 
     286view ray-plane intersections.  Furthermore, the hierarchical 
     287structures can be exploited as hierarchy of view cells. Interior nodes 
     288form larger view cells containing the children. If necessary, a leaf 
     289can be easily subdivided into smaller view cells. 
     290 
     291Currently we consider three different approaches to generate the 
     292initial view cell BSP tree.  The third method is not restricted to BSP 
     293trees, but BSP trees are preferred  because of their greater 
     294flexibility. 
    289295 
    290296 
    … …  
    303309  \#splits & 25 & 188936\\\hline\hline 
    304310  max tree depth & 13 & 27\\\hline\hline 
    305   avg tree depth & 9.747 & 21.11\\\hline\hlien 
     311  avg tree depth & 9.747 & 21.11\\\hline\hline 
    306312   
    307313 \end{tabular} 
    … …  
    312318\begin{itemize} 
    313319 
    314 \item We use a number of input view cells given in advance. As input view cell  
    315 any closed mesh can be applied. The only requirement is that the 
    316 any two view cells do not overlap.  
    317 First the view cell polygons are extracted, and the BSP is build from 
    318 these polygons using some global optimizations like tree balancing or 
    319 least splits. Then one view cell after the other 
    320 is inserted into the tree to find out the leafes where they are contained 
    321 in. The polygons of the view cell are filtered down the tree, 
    322 guiding the insertion process. Once we reach a leaf and there are no 
    323 more polygons left, we terminate the tree subdivision. If we are on 
    324 the inside of the last split plane (i.e., the leaf represents the 
    325 inside of the view cell), we associate the leaf with the view cell 
    326 (i.e., add a pointer to the view cell). One input view cell can 
    327 be associated with many leafes, whereas each leafs has only one view cell. 
    328 Some statistics about using this method on the vienna view cells set are given in table~\ref{tab:viewcell_bsp}. 
    329 However, sometimes a good set of view cells is not available. Or the scene 
    330 is changed frequently, and the designer does not want to create new view cells on each change. 
    331 In such a case one of the following two methods should rather be chosen, which generate view cells 
    332 automatically. 
     320\item We use a number of input view cells given in advance. As input 
     321view cell  any closed mesh can be applied. The only requirement is 
     322that the any two view cells do not overlap.  First the view cell 
     323polygons are extracted, and the BSP is build from these polygons using 
     324some global optimizations like tree balancing or least splits. Then 
     325one view cell after the other is inserted into the tree to find out 
     326the leaves where they are contained in. The polygons of the view cell 
     327are filtered down the tree, guiding the insertion process. Once we 
     328reach a leaf and there are no more polygons left, we terminate the 
     329tree subdivision. If we are on the inside of the last split plane 
     330(i.e., the leaf represents the inside of the view cell), we associate 
     331the leaf with the view cell (i.e., add a pointer to the view 
     332cell). One input view cell can be associated with many leaves, whereas 
     333each leafs has only one view cell.  Some statistics about using this 
     334method on the Vienna view cells set are given in 
     335table~\ref{tab:viewcell_bsp}.  However, sometimes a good set of view 
     336cells is not available. Or the scene is changed frequently, and the 
     337designer does not want to create new view cells on each change.  In 
     338such a case one of the following two methods should rather be chosen, 
     339which generate view cells automatically. 
    333340 
    334341 
    335342\item We apply a BSP tree subdivision to the scene geometry. Whenever 
    336 the subdivision terminates in a leaf, a view cell is associated with the leaf node. 
    337 This simple approach is justified because it places the view cell borders  
    338 along some discontinuities in the visibility function. 
    339  
    340 \item  The view cell generation can be guided by the sampling process. We start with 
    341 with a single initial view cell representing the whole space. If a given threshold 
    342 is reached during the preprocessing (e.g., the view cell is pierced by too many rays  
    343 resulting in a large PVS), the view cell is subdivided into smaller cells using some criteria. 
    344  
    345 In order to evaluate the best split plane, we first have to define the characteristics of a 
    346 good view cell partition:  The view cells should be quite large, while their PVS stays rather small. 
    347 The PVS of each two view cells should be as distinct as possible, otherwise they could be 
    348 merged into a larger view cell if the PVSs are too similar.  
    349 E.g., for a building, the perfect view cells are usually the single rooms connected by portals. 
    350  
    351 Hence we can define some useful criteria for the split: 1) the number of rays should be roughly equal among the new view cells. 2) The split plane should be chosen in a way that the rays are maximal distinct, i.e., the number 
    352 of rays contributing to more than one cell should be minimized => the PVSs are also distinct. 3) For BSP trees, 
    353 the split plane should be aligned with some scene geometry which is large enough to contribute 
    354 a lot of occlusion power. This criterium can be naturally combined with the second one. 
    355 As termination criterium we can choose the minimum PVS / piercing ray size or the maximal tree depth. 
     343the subdivision terminates in a leaf, a view cell is associated with 
     344the leaf node.  This simple approach is justified because it places 
     345the view cell borders  along some discontinuities in the visibility 
     346function. 
     347 
     348\item  The view cell generation can be guided by the sampling 
     349process. We start with with a single initial view cell representing 
     350the whole space. If a given threshold is reached during the 
     351preprocessing (e.g., the view cell is pierced by too many rays 
     352resulting in a large PVS), the view cell is subdivided into smaller 
     353cells using some criteria. 
     354 
     355In order to evaluate the best split plane, we first have to define the 
     356characteristics of a good view cell partition:  The view cells should 
     357be quite large, while their PVS stays rather small.  The PVS of each 
     358two view cells should be as distinct as possible, otherwise they could 
     359be merged into a larger view cell if the PVSs are too similar.  E.g., 
     360for a building, the perfect view cells are usually the single rooms 
     361connected by portals. 
     362 
     363Hence we can define some useful criteria for the split: 1) the number 
     364of rays should be roughly equal among the new view cells. 2) The split 
     365plane should be chosen in a way that the ray sets are disjoint, i.e., 
     366the number of rays contributing to more than one cell should be 
     367minimized. 3) For BSP trees, the split plane should be aligned with 
     368some scene geometry which is large enough to contribute a lot of 
     369occlusion power. This criterion can be naturally combined with the 
     370second one.  As termination criterion we can choose the minimum PVS / 
     371piercing ray size or the maximal tree depth. 
    356372\end{itemize} 
    357373 
    358 In the future we aim to extend the view cell construction by using 
    359 feedback from the PVS computation: the view cells which contain many 
    360 visibility changes will be subdivided further and neighboring view 
    361 cells with similar PVSs will be merged. We want to gain a more precise 
    362 information about visibility by selectivly storing rays with the 
    363 viewcells and computing visibility statistics for subsets of rays 
    364 which intersect subregions of the given view cell. 
    365  
     374 
     375% In the future we aim to extend the view cell construction by using 
     376% feedback from the PVS computation: the view cells which contain many 
     377% visibility changes will be subdivided further and neighboring view 
     378% cells with similar PVSs will be merged. We want to gain a more precise 
     379% information about visibility by selectively storing rays with the 
     380% view cells and computing visibility statistics for subsets of rays 
     381% which intersect subregions of the given view cell. 
    366382 
    367383\subsection{From-object based visibility} 
    … …  
    386402At this phase of the computation we not only start the samples from 
    387403the objects, but we also store the PVS information centered at the 
    388 objects. Instead of storing a PVS consting of objects visible from 
     404objects. Instead of storing a PVS consisting of objects visible from 
    389405view cells, every object maintains a PVS consisting of potentially 
    390406visible view cells. While these representations contain exactly the 
    … …  
    399415of the algorithm visits scene objects sequentially. For every scene 
    400416object we randomly choose a point on its surface. Then a ray is cast 
    401 from the selected point according to the randomly chosen direction. We 
    402 use a uniform distribution of the ray directions with respect to the 
    403 halfspace given by the surface normal. Using this strategy the samples 
    404 at deterministicaly placed at every object, with a randomization of 
    405 the location on the object surface. The uniformly distributed 
    406 direction is a simple and fast strategy to gain initial visibility 
    407 information. 
     417from the selected point according to the randomly chosen direction 
     418(see Figure~\ref{fig:sampling}). We use a uniform distribution of the 
     419ray directions with respect to the half space given by the surface 
     420normal. Using this strategy the samples at deterministically placed at 
     421every object, with a randomization of the location on the object 
     422surface. The uniformly distributed direction is a simple and fast 
     423strategy to gain initial visibility information. 
    408424 
    409425\begin{figure}%[htb] 
    410   \includegraphics[width=0.6\textwidth, draft=\DRAFTFIGS]{figs/sampling} \\ 
     426  \centerline{ 
     427    \includegraphics[width=0.4\textwidth, draft=\DRAFTFIGS]{figs/sampling} 
     428  } 
     429   
    411430%\label{tab:online_culling_example} 
    412431  \caption{Three objects and a set of view cells corresponding to leaves 
    … …  
    414433    samples cast from a selected object (shown in red). Note that most 
    415434    samples contribute to more view cells.  } 
    416   \label{fig:online_culling_example} 
     435  \label{fig:sampling} 
    417436\end{figure} 
    418437 
    … …  
    444463local view cell directional density. This means placing more samples at 
    445464directions where more view cells are located: We select a random 
    446 viecell which lies at the halfpace given by the surface normal at the 
     465view cell which lies at the half space given by the surface normal at the 
    447466chosen point. We pick a random point inside the view cell and cast a 
    448467ray towards this point. 
    … …  
    451470\subsection{Accounting for Visibility Events} 
    452471 
    453  Visibility events correspond to appearance and disapearance of 
     472 Visibility events correspond to appearance and disappearance of 
    454473objects with respect to a moving view point. In polygonal scenes the 
    455474events defined by event surfaces defined by three distinct scene 
    456475edges. Depending on the edge configuration we distinguish between 
    457 vertex-edge events (VE) and tripple edge (EEE) events. The VE surfaces 
     476vertex-edge events (VE) and triple edge (EEE) events. The VE surfaces 
    458477are planar planes whereas the EEE are in general quadratic surfaces. 
    459478 
    … …  
    467486connectivity information we select a random silhouette edge from this 
    468487object and cast a sample which is tangent to that object at the 
    469 selectededge . 
     488selected edge . 
    470489 
    471490The second strategy works as follows: we randomly pickup two objects 
    … …  
    483502\section{Summary} 
    484503 
    485 This chapter described a global visibility sampling tool which forms a 
    486 core of the visibility preprocessing framework. The global visibility 
    487 sampling computes aggresive visibility, i.e. it computes a subset of 
    488 the exact PVS for each view cell. The aggresive sampling provides a 
    489 fast progressive solution and thus it can be easily integrated into 
    490 the game development cycle. The sampling itself involves several 
    491 strategies which aim to pregressively discover more visibility 
    492 relationships in the scene. 
     504This chapter described the global visibility sampling algorithm which 
     505forms a core of the visibility preprocessing framework. The global 
     506visibility sampling computes aggressive visibility, i.e. it computes a 
     507subset of the exact PVS for each view cell. The aggressive sampling 
     508provides a fast progressive solution and thus it can be easily 
     509integrated into the game development cycle. The sampling itself 
     510involves several strategies which aim to progressively discover more 
     511visibility relationships in the scene. 
    493512 
    494513The methods presented in this chapter give a good initial estimate 
    495514about visibility in the scene, which can be verified by the mutual 
    496 visibility tool described in the next chapter. 
    497  
    498  
    499  
    500  
     515visibility algorithms described in the next chapter. 
     516 
     517 
     518 
     519 
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