In this paper we introduce the Progressive Forest Split (PFS) representation, a new adaptive refinement scheme for storing and transmitting manifold triangular meshes in progressive and highly compressed form. As in the Progressive Mesh (PM) method of Hoppe, a triangular mesh is represented as a low resolution polygonal model followed by a sequence of refinement operations, each one specifying how to add triangles and vertices to the previous level of detail to obtain a new level. The PFS format shares with PM and other refinement schemes the ability to smoothly interpolate between consecutive levels of detail. However, it achieves much higher compression ratios than PM by using a more complex refinement operation which can, at the expense of reduced granularity, be encoded more efficiently. A forest split operation doubling the number n of triangles of a mesh requires a maximum of approximately 3:5n bits to represent the connectivity changes, as opposed to approximately #5 + log 2 #n## n bits in PM. We describe

Efficient algorithms for compressing geometric data have been widely developed in the recent years, but they are mainly designed for closed polyhedral surfaces which are manifold or “nearly manifold”. We propose here a progressive geometry compression scheme which can handle manifold models as well as “triangle soups ” and 3D tetrahedral meshes. The method is lossless when the decompression is complete which is extremely important in some domains such as medical or finite element. While most existing methods enumerate the vertices of the mesh in an order depending on the connectivity, we use a kd-tree technique [8] which does not depend on the connectivity. Then we compute a compatible sequence of meshes which can be encoded using edge expansion [14] and vertex split [24]. 1 The main contributions of this paper are: the idea of using the kd-tree encoding of the geometry to drive the construction of a sequence of meshes, an improved coding of the edge expansion and vertex split since the vertices to split are implicitly defined, a prediction scheme which reduces the code for simplices incident to the split vertex, and a new generalization of the edge expansion operation to tetrahedral meshes. 1

Abstract—Unstructured tetrahedral meshes are commonly used in scientific computing to represent scalar, vector, and tensor fields in three dimensions. Visualization of these meshes can be difficult to perform interactively due to their size and complexity. By reducing the size of the data, we can accomplish real-time visualization necessary for scientific analysis. We propose a two-step approach for streaming simplification of large tetrahedral meshes. Our algorithm arranges the data on disk in a streaming, I/O-efficient format that allows coherent access to the tetrahedral cells. A quadric-based simplification is sequentially performed on small portions of the mesh in-core. Our output is a coherent streaming mesh which facilitates future processing. Our technique is fast, produces high quality approximations, and operates out-of-core to process meshes too large for main memory. Index Terms—Computational geometry and object modeling, out-of-core algorithms, streaming algorithms, mesh simplification, large meshes, tetrahedral meshes. 1