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Seminar on Industrial Problems

Geometric Compression of Complex 3D Data Bases

October 3, 1997

Presented by:

Image of Gabriel
Taubin

Gabriel Taubin, taubin@watson.ibm.com
Visual and Geometric Computing
IBM T. J. Watson Research Center
IBM, Yorktown Heights


The primary means of representing 3D objects for industry and consumer use and for transmission over the Internet is through polyhedron models. Polyhedron models are particularly effective for hardware-assisted rendering, which is important for video games, virtual reality, fly-through, and electronic mock-up applications involving complex CAD models. In comparison to image and video compression, little attention has been devoted to the compression of 3D shapes. However, this situation is like ly to change rapidly:

 

    1. The increasing complexity of CAD models raises significantly the cost of the memory and storage required by these models.
    2. The distribution of 3D models over networks for collaborative design, gaming, rapid prototyping or virtual interactions is seriously limited by the available bandwidth.
    3. The graphics performance of high level hardware adapters is limited by insufficient on-board memory to store the entire model or by a data transfer bottleneck.

 

The Virtual Reality Modeling Language (VRML), sometimes pronounced verml, is rapidly becoming the standard file format for transmitting 3D virtual worlds across the Int ernet. Unfortunately, even what would be considered small VRML files tend to be quite large. The workhorse VRML node that is designed to contain geometry is the IndexedFaceSet node (IFS). Any polygonal shape can be described with an IFS, but this is expe nsive in terms of storage requirements. (Since arbitrary polygonal faces may easily triangulated, we restrict the exposition to triangular meshes.) A typical VRML file containing an object of a couple hundred faces will have a size of about 30 kilobytes. The description of a large virtual world can easily reach 10 megabytes. One easy way to reduce the storage requirements is to use a binary rather than the standard ASCII representation of coordinates. Another standard technique involves removing redunda nt vertex references. In VRML several topologically adjacent faces store the same vertex index. (In particular, 3 vertices are stored for every triangular face.) Models can be compressed by constructing triangle strips and triangle fans, so that a single vertex defines each new triangle in the strip and redundancy is reduced. However, as the speaker showed, the most dramatic reductions in storage requirements result from the topologically assisted compression of the IFS. Here, the specific geometry of t he mesh can be used to reduce storage requirements.

 

The speaker described the concepts behind IBM's Geometric Compression Technology, originally presented in the IBM Research technical report number RC-20340 (January 16, 1996), "Geometric Compression Through Topological Surgery", by Gabriel Taubin and Jarek Rossignac. Gabriel Taubin, Bill Horn, and Francis Lazarus have extended the technology to handle features found in VRML97. Compression ratios of up to 50:1 or more can be obtai ned for large VRML97 models. The algorithms compress connectivity data without loss of information, to approximately two bits per triangle.

 

We briefly describe the method of Taubin and Rossignac for representing triangular meshes in compressed form. A triangular mesh is defined by the location of its vertices (positions) and by the association between each triangle and its sustaining vert ices (connectivity). To develop the basic concepts, we first concentrate on simple triangular meshes: triangulated connected oriented manifolds without boundary, of Euler characteristic 2, i.e., meshes that can be continuously deformed into a sphe re. (Recall that the Euler characteristic of a triangular mesh of V vertices, E edges and T triangles is V-E+T. This number is 2 for a mesh that can be continuously deformed to a sphere and 1 for a mesh that can be continuousl y deformed to a topological disk.)

 

In the compression scheme, the connectivity information of the mesh is encoded by first constructing a vertex spanning tree in the graph of vertices and edges. (Recall that a vertex spanning tree is a connected graph with the same vertices and a subset of the edges of the original graph, and which contains no cycles. It is not unique.) The vertices of a vertex spanning tree can be divided into leaf nodes (vertices ad jacent to a single edge) and branch nodes (vertices adjacent to two edges).

 

The branching nodes and leaf nodes decompose the tree into vertex runs. A vertex run is a sequence of edges connecting a starting leaf or branching node to subsequent re gular nodes and ending in a leaf or branching node. The vertex spanning tree is represented as an array of triplets, each triplet is composed of a length, a branching bit, and a leaf bit, and describing a vertex run. These tri plets are ordered according to when the corresponding runs are visited during depth-first traversal of the tree starting from the root leaf node. Runs with a common first node are ordered according to the global orientation of the mesh. The length is the number of edges of the run, the branching bit indicates whether the run is the last run sharing the current branching node or not, and the leaf bit indicates if the run ends in a leaf or branching node. This representation efficiently encodes the structure of the vertex spanning tree. To increase the compression ratio, the compression algorithm attempts to build vertex spanning trees with the least number of runs. A peeling an orange approach seems to be most efficient.

 

Now, to encode the connectivity, the mesh is cut through the edges (called the cut edges) of the vertex spanning tree. (Thus each cut edge corresponds to exactly two boundary edges of the new mesh.) The result is a triangulated simply connected polygon, i.e., is continuously deformable to a topological disk.

 

Indeed the result of cutting through the vertex spanning tree is a connected oriented triangular mesh with boundary forming a single bounding loop of edges and no internal vertic es. Furthermore, a spanning tree of V nodes has V-1 edges, so the bounding loop has 2V-2 edges and vertices. Therefore, the resulting mesh has 2V-2, vertices, E+V-1 edges, and T triangles. Thus the Euler characte ristic is

 

(2V-2)-(E+V-1)+T=(V-E+T)-1=1.

 

Now consider the dual graph of this polygon. (Recall that the vertices of the dual graph are the triangles of the polygon. Two vertices of the dual graph are joined by an edge if and only if the corresponding triangles of the original graph are adjace nt.) Clearly, the dual graph of the polygon forms a binary spanning tree of the triangles of the mesh, which can also be decomposed into runs. This triangle spanning tree is e ncoded in the same way as the vertex spanning tree is encoded. Together, the vertex and triangle spanning trees permit the recovery of the length and boundary of each triangle run and the vertices that bound each triangle.

 

The internal edges of the polygon are called marching edges. Within each triangle run, each marching edge shares a vertex with the previous marching edge in the run. That shared vertex could lie on the left or the right boundary. A single bit of information per marching edge is used to encode the correct side.

 

Although there are other compression techniques used in the authors' method to reduce the size of the files needed to encode the data for a triangular mesh, the above construction is the main idea. The compression algorithm involves the construction and encoding of the vertex spanning tree and the triangle spanning tree. Mainly the total number of runs of the vertex and triangle trees determines the compression ratio, and optimal compression is achieved by minimizing this number. (This combinatorial optimization problem is conjectured to be NP complete, but the authors have found deterministic methods that produce good results.) The decompression algorithm generates the original mesh from the vertex and triangle spanning trees. The method can fairly easily be generalized to more general meshes with arbitrary Euler characteristic, with boundary, non-or ientable, etc. Further, it can be generalized to take into account properties of the mesh such as normals, colors, and texture mapping vectors used for shading purposes.

 

The authors have found that compression ratios of 50 to 1 with respect to standard VRML files are not unusual. Other existing compression methods either do not preserve connectivity of the mesh, or do not achieve these compression ratios.

 

There are a number of open problems connected with this construction. First of all, the compression algorithm involves a number of heuristics. How good are they? What is the o ptimal way to generate a spanning tree? In particular how can the length of each run be maximized and the number of runs minimized? What is the tradeoff between file size and compression time? How to compress time dependent objects in which the connectiv ity changes?

 

Biographical Information: Gabriel Taubin is Manager of the Visual and Geometric Computing group at the IBM T.J. Watson Research Center, where he leads a group of researchers in the creation of new geometric computation and image-based algori thms and technologies for 3D modeling, network-based graphics, and data visualization. Before this assignment, he spent five years as a member of the Exploratory Computer Vision group. Gabriel holds a PhD in EE from Brown University in the area of Comput er Vision, and a MSc in Pure Mathematics from the University of Buenos Aires, Argentina. He authored 9 patents, and published over 30 papers.

 

Report prepared by Willard Miller

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