source: trunk/VUT/doc/SciReport/preprocessing.tex @ 246

\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.


\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 

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
sample piercing $n$ viewcells which is bound by two distinct objects
contributes by at most $2*n$ entries to the current PVSs.

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{Progressive Randomized Sampling}




\subsection{Importance Sampling}


\section{Visibility Verification}


\subsection{Exact Verifier}


\subsection{Conservative Verifier}


\subsection{Error Bound Verifier}