|Hosting by Solid Eight Studios,PhotoTangler Collage Maker|
Harmless Algorithms - Issue 02 - Scene Traversal Algorithms
by Edward Kmett (30 April 1999)
|Return to The Archives|
This time I want to cover several forms of scene traversal algorithms
used to walk through common data structures. For the purposes of this
column I will be assuming that the reader is familiar with BSPs and
the concept of a portal. Like the fine culling systems overlap with
the sorting properties of some of these approaches, some of these
will share content with the visibility algorithms in the next
installment of this column. |
These algorithms generally require some form of visibility algorithm to be viable for a large scale scene. Some of these provide more information to a visibility algorithm than others; Some provide front-to-back ordering; Some work well in hardware environments; Some work better in software; Some inherently provide their own visibility information through the traversal.
Next time I will be covering visibility algorithms that work in conjunction with these traversals and the fine culling algorithms from the first column. Like the first one, this column is not meant to stand on its own.
Note, in the traversals below I have not incorporated the logic to fit the walk to the viewing frustum or to make the traversal obey an occlusion algorithm at all. This will be the subject of the next column.
This column is primarily intended to enumerate viable alternatives for use in engines for realtime 3d gaming that have worked in my experience. I am not attempting to cover every algorithm extant. I leave that enterprise to some ambitious grad student needing a subject for a dissertation. By no means do I wish to convey the misconception that this column can cover everything of use in 3d graphics. My goal is to point out observations and comparisons between various algorithms and provide alternate methods to a developer stuck in a rut using a given approach.
A BSP tree is quite frankly one of the most useful data structures
out there for 3d graphics. Walking a BSP tree front-to-back is
virtually identical to a conventional binary tree's inorder traversal
The only difference is the definition of which of the children is the
first node to be visited depends upon the sign of the dot product of
the viewpoint and the plane normal of the parent node. I will refer
to three of the more useful forms of polygon storage in a BSP tree below.|
A Planar BSP is what people usually think about when someone mentions a BSP tree. In a planar tree, you can effectively ignore the leaves for rendering purposes. You start with all of your polygons in one bucket, choose one via some heuristic, take that polygon and all polygons that a coplanar to it, create a BSP node, split the rest into two buckets and repeat until you have no polygon lying free in any bucket. Traversal of a Planar BSP from front-to-back is basically a variant on a standard binary tree inorder traversal.Leafy Front-to-Back:
A Leafy BSP is a useful data structure, often used by portal advocates as a quick-and-dirty means to obtain a portal set for arbitrary geometry. In addition to splitting as you walk down the BSP tree you then need to walk back up the BSP tree splitting the contents of the parent planes as you go. Afterwards you can organize the clipped polygon fragments into their assorted leaves. When this is done you no longer track which polygons lie in a given plane of the BSP tree, but instead you track which polygons act as faces for the individual BSP nodes.Quake BSP:
If you are using a front-end rasterization routine like passing directly to hardware, an S-Buffer, Active Edge List, or Active Edge Heap, then there is little purpose to splitting the polygons as you walk down the BSP tree. Simply make a second reference to it in the compiler and all is well. If you allow multiple references to a given polygon to float around the tree you can simply adjust the polygon rendering routine to keep a frame index counter like the following:
Before I cover the strengths and weaknesses of given portal traversals, I want to
cover the distinction between convex and concave node based portal engines. |
A convex node engine only allows a convex polyhedron to act as a node. Portals serve to connect individual nodes. Within a convex node engine it is quite possible to get perfect front-to-back ordering.Concave Node:
A concave node engine allows just about anything to serve as a node. It could be a room with some complex geometry, a BSP tree, an undulating mass of procedurally generating flesh, etc... A concave node engine does not lend itself to easily providing front-to-back ordering. As such it is really only suitable to frontend rasterizers such as hardware, S-Buffers, Active Edge Lists and Active Edge Heaps, like the Quake BSP approach above.With this distinction in mind. I will proceed to the traversals generally used to walk through a conventional portalized scene.
The most obvious portal traversal is recursive. The algorithm is generally implemented something like the following:Keyed Queuing:
In an effort to fix the problems with the previous algorihm, another common variant that works if the nodes are obtained from a BSP tree is to use the BSP key for a given node and viewpoint to provide a sorting criteria. This assumes that the data structure used is roughly similar to that for the Leafy BSP above with the addition of portal information for the empty faces shared with neighboring nodes. The concept of sorting by a BSP key is introduced in Michael Abrash's Black Book.Concave/PVS:
The simplest algorithm here for traversal is to allow anything to be in a node and to maintain a potentially visible set between concave nodes. The traversal is identical to the Quake BSP traversal above. Collision can be much harder to detect and handle; Collision detection is very important in a game, so consider this solution with care. Other issues include detecting what node you are presently in in the absence of convex boundaries. Usual fixes include declaring the node to be convex in shape but containing arbitrary geometry. This is a nice fast-and-loose algorithm for getting polygons on the screen though.
An octree is a very simple data structure which has recently received a bit more
hype than it is worth on its own. There is a decent Introduction to Octrees
by Jaap Suter in the tutorial section here on flipCode, so I will not attempt to
review that material here. Instead I will build upon it.|
Octrees are very well suited to particular tasks such as finding objects in a given area, or locating likely polygons to attempt collision with; They are not necessarily any better suited to dealing with a static polygon soup than another structure is. By itself an octree does not provide a means of front-to-back traversal of the individual polygons within a leaf, however there are at least two workable front-to-back traversal for walking amongst the nodes themselves.
Different uses of this structure provide different answers as to what is the best polygon count to stop at when subdividing the octree and what should be used as a maximum recursion depth.
A common implementation would resemble the following:
Ordering the contents of the octree leaves with miniature BSP trees may be useful if perfect front-to-back ordering of the polygons is desired for another purpose.
Hierarchical Front-to-Back Traversal:
Hierarchical traversal of an octree is based on the same plane separation principle that makes a BSP front-to-back walk work. In this case, think of a single node with its eight children as mimicking 3 levels of a BSP tree:Neighbor Front-to-Back Traversal:
Neighbor Traversal requires some minor slight addition to the octree definition:
A KD-Tree is nearly identical to a BSP tree. The differences between a BSP and a
KD-tree include that a KD-Tree is forced to use axis aligned planes and the current
level of the tree is used to determine which axis to split along.|
A kd-tree node subdivides space into 2 smaller spaces like a BSP node. The additional restrictions upon a kd-tree are that the planes be axis aligned and the choice of axis is based on the current level in the tree.
This is a classic kd-tree, and there exist many papers on the subject, off the top of my head Seth Teller uses kd-trees in his paper on visibility determination in densely occluded environments.
Note that a kd-tree splits on one plane at a time. This allows it to fit better than an octree, because the planes in the children can be at different offsets and because they fit to the data, allowing a relatively balanced structure.
One modification to the 'classic' kd-tree structure is to allow the choice of plane to split upon to vary at each level, this drives up your preprocessing cost by about a factor of 3, and adds a small bit of data to the tree, but can theoretically produce fewer splits, because you can choose your partitioning plane, by which plane when it would divide the child sets into two equal buckets would produce the fewest splits. Unfortunately this optimization, much like the balance vs. split heuristics in BSP tree generation can lead to degenerately shaped kd-tree nodes if left to run unchecked.
Hierarchical Front-to-Back Traversal:
Hierarchical traversal of a kd-tree is identical to hierarchical traversal of an octree as described above or to a front-to-back traversal of a BSP tree.Neighbor Front-to-Back Traversal:
Neighbor traversal of a kd-tree can be performed in a similar manner to the neighbor traversal of the octree above, but it is not as effective. The reason is because the faces of the octree children at a given level all line up perfectly. In the kd-tree using the figure above, the two y aligned planes are likely to have chosen different offsets to split upon. This means that the neighbor traversal winds up severely handicapped as the traversal algorithm has to walk down to the leaf from a higher level node as the norm rather than the exception, unlike the octree neighbor traversal.
Feudal Priority Trees:
A feudal priority tree is a form of dynamic BSP tree suited to low polygon count environments. It has more relaxed requirements than a conventional BSP tree, but the runtime cost of using it can be much higher, and it doesn't scale well.
An adaptive octree is an octree which allows the position of the split point in the center of an octree node to be displaced. In practice, I tend to prefer a kd-tree to an adaptive octree, but in some cases, the adaptive octree may consume less memory. In addition, the neighbor front-to-back traversal of an adaptive octree tends to be faster than for a kd-tree, because within a given octree node the partitioning planes line up.
In summary, if you are writing a hardware only engine, a concave node based portal engine
is probably a safe bet. The vagaries involved in collision in this environment can be
overcome. The only real difficulty is that given an arbitrary object in 3d space, it can
be a bit expensive to discover which nodes the object is touching, without tracking this
information as the object moves around the level.|
Octrees and kd-trees serve as a spatial subdivision mechanism, in and of themselves they cannot perform all polygon sorting, and in fact, on hardware you do not need polygon sorting except for surfaces with translucencies.
Next time, I will be covering methods for visibility that combine structures from both this and the previous issue.
April 29, 1999
You can contact the author at the following address: firstname.lastname@example.org
You can also check out his web site at: http://www.bloodshed.com/
This document is (C) 1999 Edward Kmett and may not be reproduced in any way without explicit permission from the author (Edward Kmett).