source: trunk/VUT/doc/SciReport/mutual.tex @ 269

\chapter{Mutual Visibility Tool}


If a fast visibility solution is required such as during the
development cycle then the aggresive visibility sampling discussed in
the previous chapter is a good candidate. However for final solution
(production) we should either be sure that there can be no visible
error due to the preprocessed visibility or the error is suffieciently
small and thus not noticable.

The verification starts with identifiying a minimal set of object/view
cell pairs which are classified as mutually invisible. We process the
objects sequentially and for every given object we determine the view
cells which form the boundary of the invisible view cell set.

This boundary it is based on the current view cell visibility
classifications. We assume that there is a defined connectivity
between the viewcells which is the case for both BSP-tree or kD-tree
based view cells. Then given a PVS consisting of visible viewcells
(with respect to an object) we can find all the neighbors of the
visible viewcells which are invisible. In order words a view cell
belongs to this boundary if it is classified invisible and has at
least one visible neighbor. If all view cells from this boundary are
proven invisible, no other viewcell (behind this boundary) can be
visible. If the verification determines some view cells as visible,
the current invisible boundary is extended and the verification is
applied on the new view cells forming the boundary.

The basic operation of the verification is mutual visibility test
between an object and a view cell. This test works with a bounding
volume of the object and the boundary of the view cell. In the most
general case both are defined bound by a convex polyhedron, in a
simpler case both are defined by an axis aligned bounding box.

Below, we describe three different mutual visibility verification
algorithms. The first algorithm which is described in most detail
computes exact visibility. The second one is a coservative algorithm
and the third one is an approximate algorithm with a guaranteed error
bound.


\section{Exact Mutual Visibility Test}

The exact mutual visibility test computes exact visibility between two
polyhedrons in the scene. This is computed by testing visibility
between all pairs of potentially visible polygons of these
polyhedrons.  For each pair of tested polygons the computation is
localized into the shaft defined by their convex hull. This shaft is
used to determine the set of relevant occluders~\cite{Haines94}.

\section{Occlusion tree}
\label{sec:rot3d_ot}

 The occlusion tree for the visibility from region problem is a 5D BSP
tree maintaining a collection of the 5D blocker polyhedra. The tree is
constructed for each source polygon $P_S$ and represents all rays
emerging from $P_S$. Each node $N$ of the tree represents a subset of
line space ${\mathcal Q^*_N}$. The root of the tree represents the
whole problem-relevant line set ${\mathcal L}_R$. If $N$ is an
interior node, it is associated with a \plucker plane $\pplane_N$.
Left child of $N$ represents ${\mathcal Q^*_N} \cap \pplane^-_N$,
right child ${\mathcal Q^*_N} \cap \pplane^+_N$, where $\pplane^-_N$
and $\pplane^+_N$ are halfspaces induced by $\pplane_N$.

 Leaves of the occlusion tree are classified $in$ or $out$. If $N$ is
an $out$-leaf, ${\mathcal Q^*_N}$ represents unoccluded rays emerging
from the source polygon $P_S$. If $N$ is an $in$-leaf, it is
associated with an occluder $O_N$ that blocks the corresponding set of
rays ${\mathcal Q^*_N}$. Additionally $N$ stores a fragment of the
blocker polyhedron $B_N$ representing ${\mathcal Q^*_N}$. The
intersection of $B_N$ and the \plucker quadric corresponds to a set of
stabbers ${\cal S}_N$ through which $O_N$ is visible from $P_S$. A 2D
example of an occlusion tree is shown at Figure~\ref{fig:ot}.

\begin{figure}[htb]
\centerline{
\includegraphics[width=0.9\textwidth,draft=\DRAFTFIGS]{figs/ot1}}
\caption{A 2D example of an occlusion tree. Rays intersecting scene polygons
are represented by polyhedra P1, P2 and P3. The occlusion tree represents a union
of the polyhedra. Note that P2 has been split during the insertion process.}
\label{fig:duality2d}
\end{figure}


%%  Consider a source polygon and a single occluder such as in
%% Figure~\ref{lines3}-(a). The corresponding elementary occlusion tree
%% has interior nodes associated with \plucker hyperplanes corresponding
%% to edges of the two polygons. 


\section{Occlusion tree construction}

\label{sec:rot3d_construct}

 The occlusion tree is constructed incrementally by inserting blocker
polyhedra in the order given by the size of the polygons. When
processing a polygon $P_j$ the algorithm inserts a polyhedron
$B_{P_SP_j}$ representing the feasible line set between the source
polygon $P_S$ and the polygon $P_j$. The polyhedron is split into
fragments that represent either occluded or unoccluded rays.

 We describe two methods that can be used to insert a blocker
polyhedron into the occlusion tree. The first method inserts the
polyhedron by splitting it using hyperplanes encountered during the
traversal of the tree. The second method identifies hyperplanes that
split the polyhedron and uses them later for the construction of
polyhedron fragments in leaf nodes.



\subsection{Insertion with splitting}

\label{sec:rot3_insertwithsplit}

 The polyhedron insertion algorithm maintains two variables --- the
current node $N_c$ and the current polyhedron fragment
$B_c$. Initially $N_c$ is set to the root of the tree and $B_c$ equals
to $B_{P_SP_j}$. The insertion of a polyhedron in the tree proceeds as
follows: If $N_c$ is an interior node, we determine the position of
$B_c$ and the hyperplane $\pplane_{N_c}$ associated with $N_c$. If
$B_c$ lies in the positive halfspace induced by $\pplane_{N_c}$ the
algorithm continues in the right subtree.  Similarly, if $B_c$ lies in
the negative halfspace induced by $\pplane_{N_c}$,  the algorithm
continues in the left subtree. If $B_c$ intersects both halfspaces, it
is split by $\pplane_{N_{c}}$ into two parts $B^+_{c}$ and $B^-_{c}$
and the algorithm proceeds in both subtrees of $N_c$ with relevant
fragments of $B_{c}$.

If $N_{c}$ is a leaf node then we make a decision depending on its
classification.  If $N_{c}$ is an $out$-leaf then $B_{c}$ is visible
and $N_c$ is replaced by the elementary occlusion tree of $B_{c}$. If
$N_{c}$ is an $in$-leaf it corresponds to already occluded rays and no
modification to the tree necessary. Otherwise we need to {\em merge}
$B_{c}$ into the tree. The merging replaces $N_{c}$ by the elementary
occlusion tree of $B_c$ and inserts the old fragment $B_{N_{c}}$ in
the new subtree.

\subsection{Insertion without splitting}
\label{sec:insertion_nosplit}

 The above described polyhedron insertion algorithm requires that the
polyhedron is split by the hyperplanes encountered during the
traversal of the occlusion tree. Another possibility is an algorithm
that only tests the position of the polyhedron with respect to the
hyperplane and remembers the hyperplanes that split the polyhedron on
the path from the root to the leaves. Reaching a leaf node these
hyperplanes are used to construct the corresponding polyhedron
fragment using a polyhedron enumeration algorithm.

 The splitting-free polyhedron insertion algorithm proceeds as
follows: we determine the position of the blocker polyhedron and the
hyperplane $\pplane_{N_c}$ associated with the current node $N_c$. If
$B_c$ lies in the positive halfspace induced by $\pplane_{N_c}$ the
algorithm continues in the right subtree.  Similarly if $B_c$ lies in
the negative halfspace induced by $\pplane_{N_c}$  the algorithm
continues in the left subtree. If $B_c$ intersects both halfspaces the
algorithm proceeds in both subtrees of $N_c$ and $\pplane_{N_c}$ is
added to the list of splitting hyperplanes with a correct sign for
each subtree. Reaching an $out$-leaf the list of splitting hyperplanes
and the associated signs correspond to halfspaces bounding the
corresponding polyhedron fragment. The polyhedron enumeration
algorithm is  applied using these halfspaces and the original
halfspaces defining the blocker polyhedron. Note that it is possible
that no feasible polyhedron exists since the intersection of
halfspaces is empty. Such a case occurs due to the conservative
traversal of the tree that only tests the position of the inserted
polyhedron with respect to the splitting hyperplanes. If the fragment
is not empty, the tree is extended as described in the previous
section.

Reaching an $in$-leaf the polygon positional test is applied. If the
inserted polygon is closer than the polygon associated with the leaf,
the polyhedron fragment is constructed and it is merged in the tree as
described in the previous section. The splitting-free polyhedron
insertion algorithm has the following properties:

\begin{itemize}
\item If the polyhedron reaches only $in$-leaves the 5D set operations
  on the polyhedron are avoided completely.
\item If the polyhedron reaches only a few leaves the application of the
  polyhedron enumeration algorithm is potentially more efficient than
  the sequential splitting. On the contrary, when reaching many
  $out$-leaves the splitting-free method makes less use of coherence,
  i.e. the polyhedron enumeration algorithm is applied independently
  in each leaf even if the corresponding polyhedra are bound by
  coherent sets of hyperplanes.
\item An existing implementation of the polyhedron enumeration
algorithm can be used~\cite{cdd_site,lrs_site}.
\end{itemize}


 The polyhedron enumeration algorithm constructs the polyhedron as an
intersection of a set of halfspaces. The polyhedron is described as a
set of {\em vertices} and {\em rays} and their adjacency to the
hyperplanes bounding the polyhedron~\cite{Fukuda:1996:DDM,avis96}. The
adjacency information is used to construct a 1D skeleton of the
polyhedron that is required for computation of the intersection with
the \plucker quadric.



\section{Visibility test}

\label{sec:rot3d_vistest}

 The visibility test classifies visibility of a given polygon with respect
to the source polygon. The test can be used to classify visibility of
a polyhedral region by applying it on the boundary faces of the region
and combining the resulting visibility states.

\subsection{Exact visibility test}

 The exact visibility for a given polyhedral region proceeds as
follows: for each face of the region facing the given source polygon
we construct a blocker polyhedron. The blocker polyhedron is then
tested for visibility by the traversal of the occlusion tree. The
visibility test proceeds as the algorithms described in
Section~\ref{sec:rot3d_construct}, but no modifications to the tree are
performed. If the polyhedron is classified as visible in all reached
leaves, the current face is fully visible. If the polyhedron is
invisible in all reached leaves, the face is invisible. Otherwise it
is partially visible since some rays connecting the face and the
source polygon are occluded and some are unoccluded. The visibility of
the whole region is computed using a combination of visibility states
of its boundary faces according to Table~\ref{table:vis_states}.


\subsection{Conservative visibility test}

\label{sec:rot3_conserva}

 The conservative visibility test provides a fast estimation of
visibility of the given region since it does not require the 5D
polyhedra enumeration. Visibility of the given face of the region is
determined by a traversal of the occlusion tree and testing the
position of the corresponding blocker polyhedron with respect to the
encountered hyperplanes as described in
Section~\ref{sec:insertion_nosplit}.  If the blocker polyhedron
reaches only $in$-leaves and the face is behind the polygon(s)
associated with the leaves, the face is classified invisible
. Otherwise, it is conservatively classified as visible.  The
visibility of the whole region is determined using a combination of
visibility of its faces as mentioned in the previous section.

\section{Optimizations}

\label{sec:rot3d_optimizations}

 Below we discuss several optimization techniques that can be used to
improve the performance of the algorithm. The optimizations do not
alter the accuracy of the visibility algorithm.
 
% \subsection{Occluder sorting}

%  Occluder sorting aims to increase the accuracy of the front-to-back
% ordering determined by the approximate occlusion sweep. Higher
% accuracy of the ordering decreases the number of the late merging of
% blocker polyhedra in the tree.  Recall that the merging extends the
% occlusion tree by a blocker polyhedron corresponding to an occluder
% that hides an already processed occluder.

%  The occluder sorting is applied when reaching a leaf node of the
% spatial hierarchy during the approximate occlusion sweep. Given a leaf
% node $N$ the occluder sorting proceeds as follows:

% \begin{enumerate}
% \item Determine a ray $r$ piercing the center of the source polygon
% $P_S$ and the node $N$.
% \item Compute intersections of $r$ and all supporting planes of the
% polygons associated with $N$.
% \item Sort the polygons according to the front-to-back order of the
% corresponding intersection points along $r$.
% \end{enumerate}

% The occluder sorting provides an exact priority order within the given
% leaf in the case that the polygons are pierced by the computed ray $r$
% and they do not intersect.


\subsection{Visibility estimation}

 The visibility estimation aims to eliminate the polyhedron
enumeration in the leaves of the occlusion tree. If we find out that
the currently processed polygon is potentially visible in the given
leaf-node (it is an $out$-leaf or it is an $in$-leaf and the
positional test reports the polygon as the closest), we estimate its
visibility by shooting random rays. We can use the current occlusion
tree to perform ray shooting in line space.  We select a random ray
connecting the source polygon and the currently processed
polygon. This ray is mapped to a \plucker point and this point is
tested for inclusion in halfspaces defined by the \plucker planes
splitting the polyhedron on the path from to root to the given
leaf. If the point is contained in all tested halfspaces the
corresponding ray is unoccluded and the algorithm inserts the blocker
polyhedron into the tree. Otherwise it continues by selecting another
random ray until a predefined number of rays was tested.

 The insertion of the blocker polyhedron devotes further
discussion. Since the polyhedron was not enumerated we do not know
which of its bounding hyperplanes really bound the polyhedron fragment
and which are redundant for the given leaf.  Considering all
hyperplanes defining the blocker polyhedron could lead to inclusion of
many redundant nodes in the tree. We used a simple conservative
algorithm that tests if the given hyperplane is bounding the
(unconstructed) polyhedron fragment. For each hyperplane $H_i$
bounding the blocker polyhedron the algorithm tests the position of
extremal lines embedded in this hyperplane with respect to each
hyperplane splitting the polyhedron. If mappings of all extremal lines
lay in the same open halfspace defined by a splitting hyperplane,
hyperplane $H_i$ does not bound the current polyhedron fragment and
thus it can be culled.


\subsection{Visibility merging}

 Visibility merging aims to propagate visibility classifications from
the leaves of the occlusion tree up into the interior nodes of the
hierarchy. Visibility merging is connected with the approximate
occlusion sweep, which simplifies the treatment of the depth of the
scene polygons.

The algorithm classifies an interior node of the occlusion tree as
occluded ($in$) if the following conditions hold:

\begin{itemize}
\item Both its children are $in$-nodes.
\item The occluders associated with both children are strictly closer than
  the closest unswept node of the spatial hierarchy.
\end{itemize}

 The first condition ensures that both child nodes correspond to
occluded nodes. The second condition ensures that any unprocessed
occluder is behind the occluders associated with the children. Using
this procedure the effective depth of the occlusion becomes
progressively smaller if more and more rays become occluded.



\section{Conservative Verifier}

A conservative verifier is a faster alternative to the exact
visibility verifier described above. The verifier is an extension of
the strong occlusion algorithm of Cohen-Or et
al.~\cite{cohen-egc-98}. In particular our verfier refines the search
for a strong occluder by using a hierarchical subdivision of space of
lines connecting the two regions tested for mutual
visibility. Initially the shaft bounding the the tested regions is
constructed. Rays bounding the shaft are traced through the scene and
we compute all intersections with the scene objects between the tested
regions. The the algorithm proceeds as follows:

\begin{itemize}
\item In the case that any ray does not intersect any object the
tested regions are classified as visibility and the algorithm
terminates.
\item If the rays intersect the same convex object (at any
depth) this object is a strong occluder with respect to the shaft and
thus it also hides all rays in the corresponding shaft.
\item If the rays do not intersect a single convex object four new
shafts are constructed by splitting both regions in half and the
process is repeated recursivelly.
\end{itemize}

If the subdivision does not terminate till reaching a predefined
subdivision depth, we conservativelly classify the tested regions as
mutually visible.


\section{Error Bound Approximate Verifier}

The approximate verifier will be based on the similar idea as the
conservative one. However it will behave differently in the finer
subdivision of the ray shafts. The idea is to use the above algorithm
as far as the shafts get small enough that we can guarantee that even
if the shaft is not blocked by the scene objects, a pixel error
induced due to omission of objects potential visible behind the shaft
is bellow a given threshold.

For the computation of the maximal error due to the current shaft we
assume that one tested region is a viewcell, whereas the other is an
object bounding box or cell of the spatial hierarchy. The threshold is
computed as follows: We first triangulate the farthest intersection
points in the shaft as seen from the view cell side of the shaft. Then
for each computed triangle we calculate a point in the viewcell which
maximizes the projected area of the triangle. The conservative
estimate of the maximal error is then given by a sum of the computed
projected areas.

%Option: - if there is sufficient number of samples from 1 + 2 and some
%of their statistic (local spatial and directional density + distances
%to the visible objects), maybe an error estimate could only be derived
%from there values.

% a) is perhaps the most interesting since this has not been done so far at all. I though about different methods how to do that (e.g. maintaining a triangulated front visible layer), but I always found some problem. I believe that the method which is almost implemented now is solid and I just wonder what performance it will achieve.

% The basic idea is the following:

% Use 2 plane parametrization for describing all lines intersecting the object and the viewcell. The line sets will be described by aligned rectangles on the two planes which allows to construct convex shafts bounded always by only 4 lines. After determing the initial rectangles bounding the whole line set perform recursive subdivsion as follows:

% 1. Cast the 4 corner rays and deterine ALL intersections with the occluding objects

% 2. If any ray is unoccluded termite the whole test with VISIBLE and extend the PVS of the object with the new viecell (and possibly more if the rays goes further). Start the verification for the new viewcells in the occluded layer.

% 3. If all 4 rays pierce the same convex mesh, (or mesh triangle for non convex meshes) terminate this branch with INVISIBLE. Note the terminating mesh need not be the front most one.

% 4. Triangulate the depth of the shaft as seen from the viecell side of the shaft and compute the maximal projected area of the 2 computed triangles. This determines the maximal pixel error we can make by terminating this branch as invisible.
% Note that here it would be the best to selected those intersections for the rays which have the most similar depths to better estimate the maximal error, i.e. the "depth triangles" need not be formed by the first visible layer.

% 5. If 4 does not terminate subdivide either the viewcell or the object rectangle depending on the sample depths and error computed.

% It is actually quite simple to create a conservative variant of the algorithm: subdivide only to a given depth and avoid test 4. Then the samples can only be terminated by a "STRONG occluder" which hides the whole shaft. This is similar to Dannys method, but here a hierarchical shaft subdivision is performed which should deliver much more accuracy.


% > Partly: area subdivision algorithm does not only cast "corner" rays, but maintains lists of the triangles for the given area. However the basic idea is the same: subdivide the set or rays until there is a trivial decision (a single convex object intersected by all corner rays) or the error is too small. The second criterion is different from the from-poin t setup, since the error cannot be evaluated in a single projection plane.
% > That is why this triangulation would be done: Imagine that the 4 corner rays of a shaft intersect some object(s) in the scene. Now connect these intersection points by creating two triangles. These triangles do not exist in the scene, but define the largest possible windows through which we can see further in the shaft. Now it is easy to find two points at the origin of the shaft which maximize the projected area of the two triangles. The sum of these areas gives a conservative estimate of the spatial angle error which can be made by not subdividing the shaft any further.
% > Is this description clearer?


% partly - I just don't see it in front of me now. How is the result influenced by which triangles are chosen? Is it so that the nearer the intersections, the bigger the error?



\section{Summary}

\label{sec:rot3d_summary}

 This chapter presented a mutual visibility tool, which determines
 whether two regions in the scene are visible.

 First, we have described an advanced exact visibility algorithm which
is a simple of modification of an algorithm for computing from-region
visibility in polygonal scenes. The key idea is a hierarchical
subdivision of the problem-relevant line set using \plucker
coordinates and the occlusion tree.  \plucker coordinates allow to
perform operations on sets of lines by means of set theoretical
operations on the 5D polyhedra. The occlusion tree is used to maintain
a union of the polyhedra that represent lines occluded from the given
region (polygon). We described two algorithms for construction of the
occlusion tree by incremental insertion of blocker polyhedra. The
occlusion tree was used to test visibility of a given polygon or
region with respect to the source polygon/region. We proposed several
optimization of the algorithm that make the approach applicable to
large scenes. The principal advantage of the exact method over the
conservative and the approximate ones is that it does not rely on
various tuning parameters that are characterizing many conservative or
approximate algorithms. On the other hand the exactness of the method
requires higher computational demands and implementation effort.

Second, we have described a conservative verifier which is an
extension of the algorithm based on strong occluders. The conservative
verifier is requires a specification of the shaft size at which the
tested regions are conservativelly classfied as visible.

Third, we have described an approximate error bound verifier which is
an extension of the algorithm based on strong occluders. The
conservative verifier is requires a specification of the shaft size at
which the tested regions are conservativelly classfied as visible.