source: GTP/trunk/Lib/Vis/Preprocessing/manual/introduction.tex @ 2066

\chapter{Introduction}%\chapter

\label{chap:introduction}

\section{Structure of the report}

  The report consists of two introductory chapters, which provide a
 theoretical background for description of the algorithms, and three
 chapters dealing with the actual visibility algorithms.

  This chapter provides an introduction to visibility by using a
 taxonomy of visibility problems and algorithms. The taxonomy is used
 to classify the later described visibility
 algorithms. Chapter~\ref{chap:analysis} provides an analysis of
 visibility in 2D and 3D polygonal scenes. This analysis also includes
 formal description of visibility using \plucker coordinates of
 lines. \plucker coordinates are exploited later in algorithms for
 mutual visibility verification (Chapter~\ref{chap:mutual}).

 Chapter~\ref{chap:online} describes a visibility culling algorithm
 used to implement the online visibility culling module. This
 algorithm can be used accelerate rendering of fully dynamic scenes
 using recent graphics hardware.  Chapter~\ref{chap:sampling}
 describes global visibility sampling algorithm which forms a core of
 the PVS computation module. This chapter also describes view space
 partitioning algorithms used in close relation with the PVS
 computation. Finally, Chapter~\ref{chap:mutual} describes mutual
 visibility verification algorithms, which are used by the PVS
 computation module to generate the final solution for precomputed
 visibility.




%% summarize the state of the art
%% visibility algorithms and their relation to the proposed taxonomy of
%% visibility problems. The second part of the survey should serve as a
%% catalogue of visibility algorithms that is indexed by the proposed
%% taxonomy of visibility problems.



\section{Domain of visibility problems}
\label{sec:prob_domain}

 Computer graphics deals with visibility problems in the context of
2D, \m25d, or 3D scenes. The actual problem domain is given by
restricting the set of rays for which visibility should be
determined.

Below we list common problem domains used and the corresponding domain
restrictions:

\begin{enumerate}
\item
  {\em visibility along a line}
  \begin{enumerate}
  \item line
  \item ray (origin + direction)
  \end{enumerate}
  \newpage
\item
  {\em visibility from a point} ({\em from-point visibility})
  \begin{enumerate}
  \item point
  \item point + restricted set of rays
    \begin{enumerate}
    \item point + raster image (discrete form)
    \item point + beam (continuous form)
    \end{enumerate}
  \end{enumerate}

  
\item
  {\em visibility from a line segment} ({\em from-segment visibility})
  \begin{enumerate}
  \item line segment
  \item line segment + restricted set of rays
  \end{enumerate}
  
\item
  {\em visibility from a polygon} ({\em from-polygon visibility})
  \begin{enumerate}
  \item polygon
  \item polygon + restricted set of rays
  \end{enumerate}
  
\item
  {\em visibility from a region} ({\em from-region visibility})
  \begin{enumerate}
    \item region
    \item region + restricted set of rays
  \end{enumerate}

\item
  {\em global visibility}
  \begin{enumerate}
    \item no further input (all rays in the scene)
    \item restricted set of rays
  \end{enumerate}
\end{enumerate}
  
 The domain restrictions can be given independently of the dimension
of the scene, but the impact of the restrictions differs depending on
the scene dimension. For example, visibility from a polygon is
equivalent to visibility from a (polygonal) region in 2D, but not in
3D.

%*****************************************************************************

\section{Dimension of the problem-relevant line set}

 The six domains of visibility problems stated in
Section~\ref{sec:prob_domain} can be characterized by the {\em
problem-relevant line set} denoted ${\cal L}_R$. We give a
classification of visibility problems according to the dimension of
the problem-relevant line set. We discuss why this classification is
important for understanding the nature of the given visibility problem
and for identifying its relation to other problems.


 For the following discussion we assume that a line in {\em primal
space} can be mapped to a point in {\em line space}. For purposes of
the classification we define the line space as a vector space where a
point corresponds to a line in the primal space\footnote{A classical
mathematical definition says: Line space is a direct product of two
Hilbert spaces~\cite{Weisstein:1999:CCE}. However, this definition
differs from the common understanding of line space in computer
graphics~\cite{Durand99-phd}}.



\subsection{Parametrization of lines in 2D}

 There are two independent parameters that specify a 2D line and thus
the corresponding set of lines is two-dimensional. There is a natural
duality between lines and points in 2D. For example a line expressed
as: $l:y=ax+c$ is dual to a point $p=(-c,a)$. This particular duality
cannot handle vertical lines. %See Figure~\ref{fig:duality2d} for an
example of other dual mappings in the plane.  To avoid the singularity
in the mapping, a line $l:ax+by+c=0$ can be represented as a point
$p_l=(a,b,c)$ in 2D projective space ${\cal
P}^2$~\cite{Stolfi:1991:OPG}. Multiplying $p_l$ by a non-zero scalar
we obtain a vector that represents the same line $l$. More details
about this singularity-free mapping will be discussed in
Chapter~\ref{chap:analysis}.


%\begin{figure}[!htb]
%\centerline{
%\includegraphics[width=0.9\textwidth,draft=\DRAFTFIGS]{figs/duality2d}}
%\caption{Duality between points and lines in 2D.}
%\label{fig:duality2d}
%\end{figure}





 To sum up: In 2D there are two degrees of freedom in description of a
line and the corresponding line space is two-dimensional. The
problem-relevant line set ${\cal L}_R$ then forms a $k$-dimensional
subset of ${\cal P}^2$, where $0\leq k \leq 2$. An illustration of the
concept of the problem-relevant line set is depicted in
%Figure~\ref{fig:classes}.


%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.8\textwidth,draft=\DRAFTFIGS]{figs/classes}}
%\caption{The problem-relevant set of lines in 2D. The ${\cal L}_R$ for
%visibility along a line is formed by a single point that is a mapping
%of the given line. The ${\cal L}_R$ for visibility from a point $p$ is
%formed by points lying on a line. This line is a dual mapping of the
%point $p$. ${\cal L}_R$ for visibility from a line segment is formed
%by a 2D region bounded by dual mappings of endpoints of the given
%segment.}
%\label{fig:classes}
%\end{figure}


\subsection{Parametrization of lines in 3D}


Lines in 3D form a four-parametric space~\cite{p-rsls-97}. A line
intersecting a given scene can be described by two points on a sphere
enclosing the scene. Since the surface of the sphere is a two
parametric space, we need four parameters to describe the line.

 The {\em two plane parametrization} of 3D lines describes a line by
points of intersection with the given two
planes~\cite{Gu:1997:PGT}. This parametrization exhibits a singularity
since it cannot describe lines parallel to these planes. See
%Figure~\ref{fig:3dlines} for illustrations of the spherical and the
two plane parameterizations.


%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.78\textwidth,draft=\DRAFTFIGS]{figs/3dlines}}
%\caption{Parametrization of lines in 3D. (left) A line can be
%  described by two points on a sphere enclosing the scene. (right) The
%  two plane parametrization describes a line by point of intersection
%  with two given planes.}
%\label{fig:3dlines}
%\end{figure}

 Another common parametrization of 3D lines are the {\em \plucker
coordinates}. \plucker coordinates of an oriented 3D line are a six
tuple that can be understood as a point in 5D oriented projective
space~\cite{Stolfi:1991:OPG}. There are six coordinates in \plucker
representation of a line although we know that the ${\cal L}_R$ is
four-dimensional. This can be explained as follows:

\begin{itemize}
  \item Firstly, \plucker coordinates are {\em homogeneous
    coordinates} of a 5D point. By multiplication of the coordinates
    by any positive scalar we get a mapping of the same line.
  \item Secondly, only 4D subset of the 5D oriented projective space
  corresponds to real lines. This subset is a 4D ruled quadric called
  the {\em \plucker quadric} or the {\em Grassman
  manifold}~\cite{Stolfi:1991:OPG,Pu98-DSGIV}.
\end{itemize}

 Although the \plucker coordinates need more coefficients they have no
singularity and preserve some linearities: lines intersecting a set of
lines in 3D correspond to an intersection of 5D hyperplanes. More
details on \plucker coordinates will be discussed in
Chapter~\ref{chap:analysis} and Chapter~\ref{chap:mutual} where they
are used to solve the from-region visibility problem.

  To sum up: In 3D there are four degrees of freedom in the
description of a line and thus the corresponding line space is
four-dimensional. Fixing certain line parameters (e.g. direction) the
problem-relevant line set, denoted ${\cal L}_R$, forms a
$k$-dimensional subset of ${\cal P}^4$, where $0\leq k \leq 4$.


\subsection{Visibility along a line}

 The simplest visibility problems deal with visibility along a single
line. The problem-relevant line set is zero-dimensional, i.e. it is
fully specified by the given line. A typical example of a visibility
along a line problem is {\em ray shooting}.

 A similar problem to ray shooting is the {\em point-to-point}
visibility.  The  point-to-point visibility determines whether the
line segment between two points is occluded, i.e. it has an
intersection with an opaque object in the scene. Point-to-point
visibility provides a visibility classification (answer 1a), whereas
ray shooting determines a visible object (answer 2a) and/or a point of
intersection (answer 3a). Note that the {\em point-to-point}
visibility can be solved easily by means of ray shooting. Another
constructive visibility along a line problem is determining the {\em
%maximal free line segments} on a given line. See Figure~\ref{fig:val}
for an illustration of typical visibility along a line problems.

%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.85\textwidth,draft=\DRAFTFIGS]{figs/val}}
%\caption{Visibility along a line. (left) Ray shooting. (center) Point-to-point visibility. (right) Maximal free line segments between two points.}
%\label{fig:val}
%\end{figure}


\subsection{Visibility from a point}

 Lines intersecting a point in 3D can be described  by two
parameters. For example the lines can be expressed by an intersection
with a unit sphere centered at the given point. The most common
parametrization describes a line by a point of intersection with a
given viewport. Note that this parametrization accounts only for a
%subset of lines that intersect the viewport (see Figure~\ref{fig:vfp}).

%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.6\textwidth,draft=\DRAFTFIGS]{figs/vfp}}
%\caption{Visibility from a point. Lines intersecting a point can be described by a
%  point of intersection with the given viewport.}
%\label{fig:vfp}
%\end{figure}

 In 3D the problem-relevant line set ${\cal L}_R$ is a 2D subset of
the 4D line space. In 2D the ${\cal L}_R$ is a 1D subset of the 2D
line space. The typical visibility from a point problem is the visible
surface determination. Due to its importance the visible surface
determination is covered by the majority of existing visibility
algorithms. Other visibility from a point problem is the construction
of the {\em visibility map} or the {\em point-to-region visibility}
that classifies a region as visible, invisible, or partially visible
with respect to the given point.


\subsection{Visibility from a line segment}

 Lines intersecting a line segment in 3D can be described by three
parameters. One parameter fixes the intersection of the line with the
segment the other two express the direction of the line. The
problem-relevant line set ${\cal L}_R$ is three-dimensional and it can
be understood as a 2D cross section of ${\cal L}_R$  swept according
to the translation on the given line segment (see
%Figure~\ref{fig:vls}).


%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.8\textwidth,draft=\DRAFTFIGS]{figs/vls}}
%\caption{Visibility from a line segment. (left) Line segment, a
%  spherical object $O$, and its projections $O^*_0$, $O^*_{0.5}$, $O^*_{1}$
%  with respect to the three points on the line segment. (right)
%  A possible parametrization of lines that stacks up 2D planes.
%  Each plane corresponds to mappings of lines intersecting a given
%  point on the line segment.}
%\label{fig:vls}
%\end{figure}

In 2D lines intersecting a line segment form a two-dimensional
problem-relevant line set. Thus for the 2D case the ${\cal L}_R$ is a
two-dimensional subset of 2D line space.


\subsection{Visibility from a region}

 Visibility from a region (or from-region visibility) involves the
most general visibility problems. In 3D the ${\cal L}_R$ is a 4D
subset of the 4D line space. In 2D the ${\cal L}_R$ is a 2D subset of
the 2D line space. Consequently, in the presented classification
visibility from a region in 2D is equivalent to visibility from a line
segment in 2D.

 A typical visibility from a region problem is the problem of {\em
region-to-region} visibility that aims to determine if the two given
regions in the scene are visible, invisible, or partially visible (see
%Figure~\ref{fig:vfr}). Another visibility from region problem is the
computation of a {\em potentially visible set} (PVS) with respect to a
given view cell. The PVS consists of a set of objects that are
potentially visible from any point inside the view cell. Further
visibility from a region problems include computing form factors
between two polygons, soft shadow algorithms or discontinuity meshing.


%\begin{figure}[htb]
%\centerline{
%\includegraphics[width=0.6\textwidth,draft=\DRAFTFIGS]{figs/vfr}}
%\caption{Visibility from a region --- an example of the region-to-region
%  visibility. Two regions and two occluders $A$, $B$
%  in a 2D scene.  In line space the region-to-region visibility can be
%  solved by subtracting the sets of lines $A^*$ and $B^*$
%  intersecting objects $A$ and $B$ from the lines intersecting both
%  regions.}
%\label{fig:vfr}
%\end{figure}


\subsection{Global visibility}

 According to the classification the global visibility problems can be
seen as an extension of the from-region visibility problems. The
dimension of the problem-relevant line set is the same ($k=2$ for 2D
and $k=4$ for 3D scenes). Nevertheless, the global visibility problems
typically deal with much larger set of rays, i.e.  all rays that
penetrate the scene. Additionally, there is no given set of reference
points from which visibility is studied and hence there is no given
priority ordering of objects along each particular line from ${\cal
L}_R$. Therefore an additional parameter must be used to describe
visibility (visible object) along each ray.


\subsection{Summary}

 The classification of visibility problems according to the dimension
of the problem-relevant line set is summarized in
Table~\ref{table:classification3D}.  This classification provides
means for understanding how difficult it is to compute, describe, and
maintain visibility for a particular class of problems. For example a
data structure representing the visible or occluded parts of the scene
for the visibility from a point problem needs to partition a 2D ${\cal
L}_R$ into visible and occluded sets of lines. This observation
conforms with the traditional visible surface algorithms -- they
partition a 2D viewport into empty/nonempty regions and associate each
nonempty regions (pixels) with a visible object. In this case the
viewport represents the ${\cal L}_R$ as each point of the viewport
corresponds to a line through that point. To analytically describe
visibility from a region a subdivision of 4D ${\cal L}_R$ should be
performed. This is much more difficult than the 2D
subdivision. Moreover the description of visibility from a region
involves non-linear subdivisions of both primal space and line space
even for polygonal scenes~\cite{Teller:1992:CAA,Durand99-phd}.

\begin{table*}[htb]
\begin{small}
\begin{center}
\begin{tabular}{|l|c|l|}
\hline
\multicolumn{3}{|c|}{2D} \\
\hline
\mc{domain} & $d({\cal L}_R)$ & \mc{problems} \\
\hline
\hline
\begin{tabular}{l}visibility along a line\end{tabular}         & 0 &  \begin{tabular}{l}ray shooting, point-to-point visibility\end{tabular}\\
\hline
\begin{tabular}{l}visibility from a point\end{tabular}         & 1 &  \begin{tabular}{l}view around a point, point-to-region visibility\end{tabular}\\
\hline
\begin{tabular}{l} visibility from a line segment \\ visibility from region \\ global visibility \end{tabular}
   & 2 & \begin{tabular}{l} region-to-region visibility, PVS \end{tabular}\\
\hline
\hline
\multicolumn{3}{|c|}{3D} \\
\hline
\mc{domain} & $d({\cal L}_R)$ & \mc{problems} \\
\hline
\hline
\begin{tabular}{l}visibility along a line\end{tabular}  & 0 & \begin{tabular}{l} ray shooting, point-to-point visibility \end{tabular}\\
\hline
\begin{tabular}{l}from point in a surface\end{tabular}  & 1 & \begin{tabular}{l} see visibility from point in 2D \end{tabular}\\
\hline
\begin{tabular}{l}visibility from a point\end{tabular}  & 2 & \begin{tabular}{l} visible (hidden) surfaces, point-to-region visibility,\\
                                      visibility map, hard shadows
                               \end{tabular} \\
\hline
\begin{tabular}{l}visibility from a line segment\end{tabular} & 3 & \begin{tabular}{l} segment-to-region visibility (rare) \end{tabular}\\
\hline
\begin{tabular}{l}visibility from a region\\global visibility\end{tabular}       & 4 & \begin{tabular}{l} region-region visibility, PVS, aspect graph,\\
                                            soft shadows, discontinuity meshing
                                     \end{tabular} \\

\hline  
\end{tabular}
\end{center}
\end{small}
\caption{Classification of visibility problems in 2D and 3D according
to the dimension of the problem-relevant line set.}
\label{table:classification3D}
\end{table*}





\section{Classification of visibility algorithms}


The taxonomy of visibility problems groups similar visibility problems
in the same class.  A visibility problem can be solved by means of
various visibility algorithms. A visibility algorithm poses further
restrictions on the input and output data. These restrictions can be
seen as a more precise definition of the visibility problem that is
solved by the algorithm.

 Above we classified visibility problems according to the problem
domain and the desired answers. In this section we provide a
classification of visibility algorithms according to other
important criteria characterizing a particular visibility algorithm.


\subsection{Scene restrictions}
\label{sec:scene_restrictions}

Visibility algorithms can be classified according to the restrictions
they pose on the scene description.  The type of the scene description
influences the difficulty of solving the given problem: it is simpler
to implement an algorithm computing a visibility map for scenes
consisting of triangles than for scenes with NURBS surfaces. We list
common restrictions on the scene primitives suitable for visibility
computations:


\begin{itemize}
\item
  triangles, convex polygons, concave polygons,
  
\item
  volumetric data,

\item
  points,

\item
  general parametric, implicit, or procedural surfaces.

\end{itemize}

Some attributes of scenes objects further increase the complexity of the visibility computation:

\begin{itemize}

\item
  transparent objects,

\item
  dynamic objects.

\end{itemize}

The majority of analytic visibility algorithms deals with static
polygonal scenes without transparency. The polygons are often
subdivided into triangles for easier manipulation and representation.

\subsection{Accuracy}
\label{sec:accuracy}

 Visibility algorithms can be classified according to the accuracy of
the result as:

\begin{itemize}
\item exact,
\item conservative,
\item aggressive,
\item approximate.
\end{itemize}


 An exact algorithm provides an exact analytic result for the given
problem (in practice however this result is typically influenced by
the finite precision of the floating point arithmetics). A
conservative algorithm overestimates visibility, i.e. it never
misses any visible object, surface or point. An aggressive algorithm
always underestimates visibility, i.e. it never reports an invisible
object, surface or point as visible.  An approximate algorithm
provides only an approximation of the result, i.e. it can overestimate
visibility for one input and underestimate visibility for another
input.

 The classification according to the accuracy is best illustrated on
computing PVS: an exact algorithm computes an exact PVS. A
conservative algorithm computes a superset of the exact PVS.  An
aggressive algorithm determines a subset of the exact PVS.  An
approximate algorithm computes an approximation to the exact PVS that
is neither its subset or its superset for all possible inputs.

 A more precise quality measure of algorithms computing PVSs can be
expressed by the {\em relative overestimation} and the {\em relative
underestimation} of the PVS with respect to the exact PVS.  We can
define a quality measure of an algorithm $A$ on input $I$ as a tuple
$\mbi{Q}^A(I)$:

\begin{eqnarray}
  \mbi{Q}^A(I) & = & (Q^A_o(I), Q^A_u(I)), \qquad  I \in {\cal D} \\
  Q^A_o(I) & = & {|S^A(I) \setminus S^{\cal E}(I)| \over |S^{\cal E}(I)|} \\
  Q^A_u(I) & = & {|S^{\cal E}(I) \setminus S^A(I) | \over |S^{\cal E}(I)|}
\end{eqnarray}

where $I$ is an input from the input domain ${\cal D}$, $S^A(I)$ is
the PVS determined by the algorithm $A$ for input $I$ and $S^{\cal
E}(I)$ is the exact PVS for the given input. $Q^A_o(I)$ expresses the
{\em relative overestimation} of the PVS, $Q^A_u(I)$ is the {\em
relative underestimation}.

The expected quality of the algorithm over all possible inputs can be
given as:

\begin{eqnarray}
Q^A & = & E[\| \mbi{Q}^A(I) \|] \\
    & = & \sum_{\forall I \in {\cal D}} f(I).\sqrt{Q^A_o(I)^2 + Q^A_o(I)^2}
\end{eqnarray}

where f(I) is the probability density function expressing the
probability of occurrence of input $I$. The quality measure
$\mbi{Q}^A(I)$ can be used to classify a PVS algorithm into one of the
four accuracy classes according to Section~\ref{sec:accuracy}:

\begin{enumerate}
\item exact\\
  $\forall I \in {\cal D} :Q_o^A(I) = 0 \wedge Q_u^A(I) = 0$
\item conservative\\
  $\forall I \in {\cal D} : Q_o^A(I) \geq 0 \wedge Q_u^A(I) = 0$
\item aggressive \\
  $\forall I \in {\cal D} : Q_o^A(I) = 0 \wedge Q_u^A(I) \geq 0$
\item approximate \\
  $\qquad \exists I_j, I_k \in {\cal D}: Q_o^A(I_j) > 0 \wedge Q_u^A(I_k) > 0$
\end{enumerate}



\subsection{Solution space}

\label{sec:solspace}

 The solution space is the domain in which the algorithm determines
the desired result. Note that the solution space does not need to
match the domain of the result.

The algorithms can be classified as:

\begin{itemize}
  \item discrete,
  \item continuous,
  \item hybrid.
\end{itemize}

 A discrete algorithm solves the problem using a discrete solution
space; the solution is typically an approximation of the result. A
continuous algorithm works in a continuous domain and often computes an
analytic solution to the given problem. A hybrid algorithm uses both
the discrete and the continuous solution space.

 The classification according to the solution space is easily
demonstrated on visible surface algorithms: The
z-buffer~\cite{Catmull:1975:CDC} is a common example of a discrete
algorithm. The Weiler-Atherton algorithm~\cite{Weiler:1977:HSR} is an
example of a continuous one. A hybrid solution space is used by
scan-line algorithms that solve the problem in discrete steps
(scan-lines) and for each step they provide a continuous solution
(spans).

Further classification reflects the semantics of the solution
space. According to this criteria we can classify the algorithms as:

\begin{itemize}
  \item primal space (object space),
  \item line space,
    \begin{itemize}
    \item image space,
    \item general,
    \end{itemize}
  \item hybrid.
\end{itemize}

 A primal space algorithm solves the problem by studying the
visibility between objects without a transformation to a different
solution space. A line space algorithm studies visibility using a
transformation of the problem to line space. Image space algorithms
can be seen as an important subclass of line space algorithms for
solving visibility from a point problems in 3D. These algorithms cover
all visible surface algorithms and many visibility culling
algorithms. They solve visibility in a given image plane that
represents the problem-relevant line set ${\cal L}_R$ --- each ray
originating at the viewpoint corresponds to a point in the image plane.

 The described classification differs from the sometimes mentioned
understanding of image space and object space algorithms that
incorrectly considers all image space algorithms discrete and all
object space algorithms continuous.


%*****************************************************************************



\section{Summary}

The presented taxonomy classifies visibility problems independently of
their target application. The classification should help to understand
the nature of the given problem and it should assist in finding
relationships between visibility problems and algorithms in different
application areas.  The algorithms address the following classes of
visibility problems:

\begin{itemize}
\item Visibility from a point in 3D $d({\cal L}_R)=2$.
\item Global visibility in 3D $d({\cal L}_R)=4$.
\item Visibility from a region in 3D, $d({\cal L}_R)=4$.
\end{itemize}

 This chapter discussed several important criteria for the
classification of visibility algorithms. This classification can be
seen as a finer structuring of the taxonomy of visibility problems. We
discussed important steps in the design of a visibility algorithm that
should also assist in understanding the quality of a visibility
algorithm. According to the classification the visibility algorithms
described later in the report address algorithms with the following
properties:

\begin{itemize}
\item Domain:
  \begin{itemize}
  \item viewpoint (online visibility culling),
  \item global visibility (global visibility sampling)
  \item polygon or polyhedron (mutual visibility verification)
  \end{itemize}
\item Scene restrictions (occluders):
  \begin{itemize}
  \item meshes consisting of convex polygons
  \end{itemize}
\item Scene restrictions (group objects):
  \begin{itemize}
  \item bounding boxes
  \end{itemize}
\item Output:
  \begin{itemize}
  \item PVS
  \end{itemize}
\item Accuracy:
  \begin{itemize}
  \item conservative
  \item exact
  \item aggresive
  \end{itemize}
\item Solution space:
  \begin{itemize}
  \item discrete (online visibility culling, global visibility sampling, conservative and approximate algorithm from the mutual visibility verification)
  \item continuous (exact algorithm from mutual visibility verification)
  \end{itemize}
  \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)
  \item Use of coherence of visibility:
    \begin{itemize}
    \item spatial coherence (all algorithms)
    \item temporal coherence (online visibility culling)
    \end{itemize}
  \item Output sensitivity: expected in practice (all algorithms)
  \item Acceleration data structure: kD-tree (all algorithms)
  \item Use of graphics hardware: online visibility culling
  \end{itemize}