FreeLence ‐ Coding with Free Valences

Kälberer, Felix; Polthier, Konrad; Reitebuch, Ulrich; Wardetzky, Max

doi:10.1111/j.1467-8659.2005.00872.x

Cited by 41 publications

(41 citation statements)

References 29 publications

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“…The first remark is that the rates obtained for the models fandisk, blob, horse, ford, UNC powerplant are not especially competitive with regard to the best current methods (for example, [18], whose algorithm yields 0.74 bpv for the fandisk, and 0.96 bpv for the horse). The main reason is that the vertex set of these meshes are clearly not -sample and therefore, not favorable to the convection algorithm (but note that on these meshes, we gain on the geometry coding by using the GD coder, as shown in [37]).…”

Section: Resultsmentioning

confidence: 92%

“…Regarding the methods [15], [16], [18], derived from the Touma and Gotsman's coding principle [5], they obtain rates around 1.5 bpv for usual meshes, and can achieve very low rates for highly regular meshes where vertex degrees are almost constant. We do not have any result of these algorithms for meshes fully included in Delaunay, but there is no particular reason why their rates would be better in such cases.…”

Section: Resultsmentioning

confidence: 99%

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Reconstruction Algorithms as a Suitable Basis for Mesh Connectivity Compression

Chaine

Gandoin

Roudet

2009

IEEE Trans. Automat. Sci. Eng.

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During a highly productive period running from 1995 to about 2002, the research in lossless compression of surface meshes mainly consisted in a hard battle for the best bitrates. However, for a few years, compression rates seem stabilized around 1.5 bit per vertex for the connectivity coding of usual triangular meshes, and more and more work is dedicated to remeshing, lossy compression, or gigantic mesh compression, where memory access and CPU optimizations are the new priority. However, the size of 3D models keeps growing, and many application fields keep requiring lossless compression. In this paper, we present a new contribution for single-rate lossless connectivity compression, which first brings improvement over current state of the art bitrates, and second, does not constraint the coding of the vertex positions, offering therefore a good complementarity with the best performing geometric compression methods. The initial observation having motivated this work is that very often, most of the connectivity part of a mesh can be automatically deduced from its geometric part using reconstruction algorithms. This has already been used within the limited framework of projectable objects (essentially, terrain models and GIS), but finds here its first generalization to arbitrary triangular meshes, without any limitation regarding the topological genus, the number of connected components, the manifoldness or the regularity. This can be obtained by constraining and guiding a Delaunay-based reconstruction algorithm so that it outputs the initial mesh to be coded. The resulting rates seem extremely competitive when the meshes are fully included in Delaunay, and are still good compared to the state-of-the-art in the case of scanned models.Note to Practitioners-A 3D triangle mesh is composed of a geometric part (the vertex coordinates) and a connectivity part (the description of the triangles). In this paper, we show how to reencode such surface meshes in order to obtain near zero connectivity cost for some class of surface meshes (and very good rates in the general case), while guaranteeing in the same time state-of-the-art geometry encoding cost. This method can be useful in all application areas where the mesh size is a bottleneck (typically network or storage applications). The best results are obtained for meshes made from 3D scans (in contrast to CAD meshes). The main current limitations of the method are the computing times (about 1 s per 1000 points part of the mesh, for compression which can be done offline) and the memory footprint.

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Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Reconstruction Algorithms as a Suitable Basis for Mesh Connectivity Compression

Chaine

Gandoin

Roudet

2009

IEEE Trans. Automat. Sci. Eng.

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“…Mesh compression methods on the other hand (see Kälberer et al [2005] and references therein compress only the surface itself and possibly the normals. Even though it is indeed feasible to compute differential properties from meshes [Desbrun et al 1999], this is generally not an optimal storage format for implicit surfaces like level sets.…”

Section: Compression Methodsmentioning

confidence: 99%

Out-of-core and compressed level set methods

Nielsen¹,

Nilsson

Söderström

et al. 2007

ACM Trans. Graph.

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This article presents a generic framework for the representation and deformation of level set surfaces at extreme resolutions. The framework is composed of two modules that each utilize optimized and application specific algorithms: 1) A fast out-of-core data management scheme that allows for resolutions of the deforming geometry limited only by the available disk space as opposed to memory, and 2) compact and fast compression strategies that reduce both offline storage requirements and online memory footprints during simulation. Out-of-core and compression techniques have been applied to a wide range of computer graphics problems in recent years, but this article is the first to apply it in the context of level set and fluid simulations. Our framework is generic and flexible in the sense that the two modules can transparently be integrated, separately or in any combination, into existing level set and fluid simulation software based on recently proposed narrow band data structures like the DT-Grid of Nielsen and Museth [2006] and the H-RLE of Houston et al. [2006]. The framework can be applied to narrow band signed distances, fluid velocities, scalar fields, particle properties as well as standard graphics attributes like colors, texture coordinates, normals, displacements etc. In fact, our framework is applicable to a large body of computer graphics problems that involve sequential or random access to very large co-dimension one (level set) and zero (e.g. fluid) data sets. We demonstrate this with several applications, including fluid simulations interacting with large boundaries (≈ 1500 3 ), surface deformations (≈ 2048 3 ), the solution of partial differential equations on large surfaces (≈ 4096 3 ) and mesh-to-level set scan conversions of resolutions up to ≈ 35000 3 (7 billion voxels in the narrow band). Our out-of-core framework is shown to be several times faster than current state-of-the-art level set data structures relying on OS paging. In particular we show sustained throughput (grid points/sec) for gigabyte sized level sets as high as 65% of state-of-the-art throughput for in-core simulations. We also demonstrate that our compression techniques out-perform state-of-the-art compression algorithms for narrow bands.

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“…But the order imposed by the connectivity coding is not necessarily favorable to this prediction, as shown by the results obtained on usual meshes by classical algorithms : the best of them reduce the connectivity information to less than 1 or 2 bits per vertex, while for the geometric part, the rates rarely go down under 90% of the initial size (except for very low quantizations). Among the works that follow this principle, we focus here in single-rate methods, which code and decode the mesh in one pass and do not allow progressive visualization [Deering 1995;Evans et al 1996;Taubin and Rossignac 1998;Gumhold and Strasser 1998;Touma and Gotsman 1998;Li and Kuo 1998;Bajaj et al 1999a;Bajaj et al 1999b;Gumhold et al 1999;Rossignac 1999;Rossignac and Szymczak 1999;King and Rossignac 1999;Isenburg and Snoeyink 1999;Isenburg 2000;Alliez and Desbrun 2001;Lee et al 2002;Coors and Rossignac 2004;Kaelberer et al 2005;Jong et al 2005]. To understand how these methods compare, the reader can also refer to the following surveys [Alliez and Gotsman 2004;Gotsman et al 2002;.…”

Section: Mesh Compressionmentioning

confidence: 98%

Mesh connectivity compression using convection reconstruction

Chaine

Gandoin

Roudet

2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

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During a highly productive period running from 1995 to about 2002, the research in lossless compression of 3D meshes mainly consisted in a hard battle for the best bitrates. But for a few years, compression rates seem stabilized around 1.5 bit per vertex for the connectivity coding of usual meshes, and more and more work is dedicated to remeshing, lossy compression, or gigantic mesh compression, where memory and CPU optimizations are the new priority. However, the size of 3D models keeps growing, and many application fields keep requiring lossless compression. In this paper, we present a new contribution for single-rate lossless connectivity compression, which first brings improvement over current state of the art bitrates, and secondly, does not constraint the coding of the vertex positions, offering therefore a good complementarity with the best performing geometric compression methods. The initial observation having motivated this work is that very often, most of the connectivity part of a mesh can be automatically deduced from its geometric part using reconstruction algorithms. This has already been used within the limited framework of projectable objects (essentially terrain models and GIS), but finds here its first generalization to arbitrary triangular meshes, without any limitation regarding the topological genus, the number of connected components, the manifoldness or the regularity. This can be obtained by constraining and guiding a Delaunay-based reconstruction algorithm so that it outputs the initial mesh to be coded. The resulting rates seem extremely competitive when the meshes are fully included in Delaunay, and are still good compared to the state of the art in the general case.

show abstract

FreeLence ‐ Coding with Free Valences

Cited by 41 publications

References 29 publications

Reconstruction Algorithms as a Suitable Basis for Mesh Connectivity Compression

Reconstruction Algorithms as a Suitable Basis for Mesh Connectivity Compression

Out-of-core and compressed level set methods

Mesh connectivity compression using convection reconstruction

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