Spectral compression of mesh geometry

Karni, Zachi; Gotsman, Craig

doi:10.1145/344779.344924

Cited by 513 publications

(430 citation statements)

References 18 publications

(19 reference statements)

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“…Karni and Gotsman [23] compress the geometry of a mesh by computing its spectrum with the eigenvector decomposition of its Laplacian matrix. The spectral coefficients, after being quantized and entropy coded, are sufficient to decompress a good approximation of the initial mesh.…”

Section: Laplacian Operator-basedmentioning

confidence: 99%

Progressive compression of manifold polygon meshes

Maglo

Courbet

Alliez

et al. 2012

Computers & Graphics

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This paper presents a new algorithm for the progressive compression of manifold polygon meshes. The input surface is decimated by several traversals that generate successive levels of detail through a specific patch decimation operator which combines vertex removal and local remeshing. The mesh connectivity is encoded by two lists of Boolean error predictions based on the mesh geometry: one for the inserted edges and the other for the faces with a removed center vertex. The mesh geometry is encoded with a barycentric error prediction of the removed vertex coordinates and a local curvature prediction. We also include two methods that improve the rate-distortion performance: a wavelet formulation with a lifting scheme and an adaptive quantization technique. Experimental results demonstrate the effectiveness of our approach in terms of compression rates and rate-distortion performance.

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Section: Laplacian Operator-basedmentioning

confidence: 99%

Progressive compression of manifold polygon meshes

Maglo

Courbet

Alliez

et al. 2012

Computers & Graphics

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show abstract

“…Several distortion measures have been exploited by single-rate mesh coders [16][17][18][19]. In this paper, we choose as reconstruction error the symmetric root mean square error between two surfaces [20] also called the S2S distance.…”

Section: The S2s Distance As Quality Criterionmentioning

confidence: 99%

“…Several distortion measures have been exploited for compression of irregular meshes [16][17][18][19]. For instance, Karni and Gotsman (2000) introduce a metric which captures the visual difference between the original mesh and its approximation [17].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Karni and Gotsman (2000) introduce a metric which captures the visual difference between the original mesh and its approximation [17]. Their criterion depends on the geometric distance and the laplacian difference between models.…”

Section: Introductionmentioning

confidence: 99%

“…King and Rossignac (1999) focused on the problem of balancing two forms of lossy mesh compression: reduction of the number of vertices by simplification techniques, and reduction of the bitrate per vertex coordinate [16]. More recently, Karni and Gotsman (2000) proposed to truncate spectral coefficients according to a maximum RMS value given as input parameter [17].…”

Section: Introductionmentioning

confidence: 99%

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An efficient bit allocation for compressing normal meshes with an error-driven quantization

Payan

Antonini

2005

Computer Aided Geometric Design

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We propose a new wavelet compression algorithm based on the rate-distortion optimization for densely sampled triangular meshes. This algorithm includes the normal remesher, a wavelet transform, and an original bit allocation optimizing the quantization of the wavelet coefficients. The allocation process minimizes the reconstruction error for a given bit budget. As distortion measure, we use the mean square error of the normal mesh quantization, expressed according to the quantization error of each subband. We show that this metric is a suitable criterion to evaluate the reconstruction error, i.e. the geometric distance between the input mesh and the quantized normal one. Moreover, to design a useful bit allocation, we propose a model-based approach, depending on the wavelet coefficient distributions. The proposed algorithm achieves results better than state-of-the-art methods, up to +2.5 dB compared to the original zerotree coder for normal meshes.

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Basic Background in 3D Object Processing

Lavoué¹

2008

3D Object Processing

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This chapter details the different 3D representation models, commonly used in computer graphics and 3D modeling. We distinguish three main representation schemes: polygonal meshes; surface-based models, such as parametric, implicit and subdivision surfaces; and volumetric models including primitivebased models (superquadric, hyperquadric), voxel representation and constructive solid geometry. For each of these 3D representation schemes, we analyze the advantages and drawbacks in terms of modeling, regarding different applications.The point-based representation, which has been recently introduced in computer graphics, is not within the scope of this book. Recent reviews can be found in Kobbelt and Botsch (2004) and Alexa et al. (2004).

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Spectral compression of mesh geometry

Cited by 513 publications

References 18 publications

Progressive compression of manifold polygon meshes

Progressive compression of manifold polygon meshes

An efficient bit allocation for compressing normal meshes with an error-driven quantization

Basic Background in 3D Object Processing

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