source: trunk/VUT/doc/SciReport/preprocessing.tex @ 247

\chapter{Visibility Preprocessing}


\section{Introduction}


\section{Related Work}


\section{Overview of Visibility Preprocessor}

The proposed visibility preprocessing framework consists of two major
steps.
\begin{itemize}
\item The first step is an aggresive visibility sampling which gives
initial estimate about global visibility in the scene. The sampling
itself involves several strategies which will be described in
section~\ref{sec:sampling}. The imporant property of the aggresive
sampling step is that it provides a fast progressive solution to
global visibility and thus it can be easily integrated into the
game development cycle.

\item The second step is visibility verification. This step turns the
previous aggresive visibility solution into either exact, conservative
or error bound aggresive solution. The choice of the particular
verifier is left on the user in order to select the best for a
particular scene, application context and time constrains. For
example, in scenes like a forest an error bound aggresive visibility
can be the best compromise between the resulting size of the PVS (and
framerate) and the visual quality. The exact or conservative algorithm
can however be chosen for urban scenes where of even small objects can
be more distructing for the user.
\end{itemize}


\section{Aggresive Global Visibility Sampling}

In traditional visibility preprocessing the view space is
subdivided into viewcells and for each view cell the set of visible
objects --- potentially visible set (PVS) is computed. This framewoirk
has bee used for conservative, aggresive and exact algorithms.

We propose a different strategy which has several advantages for
sampling based aggresive visibility preprocessing. The stategy is
based on the following fundamental ideas:
\begin{itemize}
\item Replace the roles of view cells and objects
\item Compute progressive global visibility instead of sequential from-region visibility
\end{itemize}

Both of these points are addressed bellow in more detail.

\subsection{From-object based visibility}

Our framework is based on the idea of sampling visibility by casting
casting rays through the scene and collecting their contributions. A
visibility sample is computed by casting a ray from an object towards
the viewcells and computing the nearest intersection with the scene
objects. All view cells pierced by the ray segment can the object and
thus the object can be added to their PVS. If the ray is terminated at
another scene object the PVS of the pierced view cells can also be
extended by this terminating object. Thus a single ray can make a
number of contributions to the progressively computed PVSs. A ray
sample piercing $n$ viewcells which is bound by two distinct objects
contributes by at most $2*n$ entries to the current PVSs. Appart from
this performance benefit there is also a benefit in terms of the
sampling density: Assuming that the view cells are usually much larger
than the objects (which is typically the case) starting the sampling
deterministically from the objects increases the probability of small
objects being captured in the PVS.

At this phase of the computation we not only start the samples from
the objects, but we also store the PVS information centered at the
objects. Instead of storing a PVSs consting of objects visible from
view cells, every object maintains a PVS consisting of potentially
visible view cells. While these representations contain exactly the
same information as we shall see later the object centered PVS is
better suited for the importance sampling phase as well as the
visibility verification phase.


\subsection{Basic Randomized Sampling}


The first phase of the sampling works as follows: At every pass of the
algorithm visits scene objects sequentially. For every scene object we
randomly choose a point on its surface. Then a ray is cast from the
selected point according to the randomly chosen direction. We use a
uniform distribution of the ray directions with respect to the
halfspace given by the surface normal. Using this strategy the samples
at deterministicaly placed at every object, with a randomization of
the location on the object surface. The uniformly distributed
direction is a simple and fast strategy to gain initial visibility
information.


The described algorithm accounts for the irregular distribution of the
objects: more samples are placed at locations containing more
objects. Additionally every object is sampled many times depending on
the number of passes in which this sampling strategy is applied. This
increases the chance of even a small object being captured in the PVS
of the view cells from which it is visible.


\subsection{Accounting for View Cell Distribution}

The first modification to the basic algorithm accounts for
irregular distribution of the viewcells. Such a case in common for
example in urban scenes where the viewcells are mostly distributed in
a horizontal direction and more viewcells are placed at denser parts
of the city. The modification involves replacing the uniformly
distributed ray direction by direction distribution according to the
local view cell density. We select a random viecell which lies at the
halfpace given by the surface normal at the chosen point. We pick a
random point inside the view cell and cast a ray towards this point.


\subsection{Accounting for Visibility Events}




\section{Visibility Verification}


\subsection{Exact Verifier}

The exact verifier computes exact mutual visibility between two
polyhedrons in the scene. This is computed by testing visibility
between all pairs of potentially polygons of these polyhedrons.





\subsection{Conservative Verifier}


\subsection{Error Bound Verifier}