This paper proposes a novel and efficient algorithm for single-rate compression of triangle meshes. The input mesh is traversed along its greedy Hamiltonian cycle in O(n) time. Based on the Hamiltonian cycle, the mesh...
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This paper proposes a novel and efficient algorithm for single-rate compression of triangle meshes. The input mesh is traversed along its greedy Hamiltonian cycle in O(n) time. Based on the Hamiltonian cycle, the mesh connectivity can be encoded by a face label sequence with low entropy containing only four kinds of labels (HETS) and the transmission delay at the decoding end that frequently occurs in the conventional single-rate approaches is obviously reduced. The mesh geometry is compressed with a global coordinate concentration strategy and a novel local parallelogram error prediction scheme. Experiments on realistic 3D models demonstrate the effectiveness of our approach in terms of compression rates and run time performance compared to the leading single-rate and progressive mesh compression methods.
This paper proposes a novel and efficient algorithm for single-rate compression of triangle meshes. The input mesh is traversed along its greedy Hamiltonian cycle in O(n) time. Based on the Hamiltonian cycle, the mesh...
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This paper proposes a novel and efficient algorithm for single-rate compression of triangle meshes. The input mesh is traversed along its greedy Hamiltonian cycle in O(n) time. Based on the Hamiltonian cycle, the mesh connectivity can be encoded by a face label sequence with low entropy containing only four kinds of labels (HETS) and the transmission delay at the decoding end that frequently occurs in the conventional single-rate approaches is obviously reduced. The mesh geometry is compressed with a global coordinate concentration strategy and a novel local parallelogram error prediction scheme. Experiments on realistic 3D models demonstrate the effectiveness of our approach in terms of compression rates and run time performance compared to the leading single-rate and progressive mesh compression methods.
In this paper, we present an adaptive-coding method for generic triangular meshes including both regular and irregular meshes. Though it is also based on iterative octree decomposition of the object space for the orig...
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In this paper, we present an adaptive-coding method for generic triangular meshes including both regular and irregular meshes. Though it is also based on iterative octree decomposition of the object space for the original mesh, as some prior arts, it has novelties in the following two aspects. First, it mathematically models the occupancy codes containing only a single-"1" bit for accurate initialization of the arithmetic coder at each octree level. Second, it adaptively prioritizes the bits in an occupancy code using a local surface smoothness measure that is based on triangle areas and therefore mitigates the effect of non-uniform vertex sampling over the surface. As a result, the proposed 3D mesh coder yields outstanding coding performance for both regular and irregular meshes and especially for the latter, as demonstrated by the experiments.
In this paper, a pure geometry compression algorithm for 3D semi-regular meshes, based on wavelet transform and space frequency quantization for 3D meshes, has been introduced. The local frame was applied to wavelet c...
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ISBN:
(纸本)9781934272350
In this paper, a pure geometry compression algorithm for 3D semi-regular meshes, based on wavelet transform and space frequency quantization for 3D meshes, has been introduced. The local frame was applied to wavelet coefficients to be more independent, and the 3D SFQ was used to quantize the coefficients. The proposed algorithm was compared with the zero-tree coder for MAPS meshes. The rate-distortion curves results are significantly better than those for zero-tree coder in the high rates.
A three-dimensional (3D) mesh compression algorithm based on novel prediction methods and a mode decision scheme is proposed in this work. After decomposing an input mesh into base and refinement layers, we segment th...
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ISBN:
(纸本)9781457713033
A three-dimensional (3D) mesh compression algorithm based on novel prediction methods and a mode decision scheme is proposed in this work. After decomposing an input mesh into base and refinement layers, we segment the geometry data of each layer into clusters. To encode vertex positions efficiently, we propose two predict ion methods: the dual ring prediction and the minimum mean square error (MMSE) prediction. Also, we develop a mode decision scheme that selects the best prediction mode for each cluster. Simulation results demonstrate that the proposed algorithm provides significantly better compression performance than conventional techniques.
We present a new 3D mesh codec for mobile devices. Mobile devices have limited resources such as low memory, show CPU speed, low power, and no floaling-point processor. Considering these mobile characteristics. we sug...
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We present a new 3D mesh codec for mobile devices. Mobile devices have limited resources such as low memory, show CPU speed, low power, and no floaling-point processor. Considering these mobile characteristics. we suggest a new approach: a hybrid of a progressive geometry and a single-rate connectivity coding. By this approach, our algorithm achieves fast visual updates of progressive 3D point clouds on a small screen of a mobile device and later finalizes restoring faces of the mesh without intensive computing. For a progressive geometry coding, an octree is created and coded. For a single-rate connectivity, coding, a new distance based algorithm is introduced. Our algorithm uses only fixed-point arithmetic with simple data structures. Our new 3D mesh codec has been tested 017 a real mobile phone and the statistical and visual results are also presented in this paper(1).
The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best qua...
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The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation ( depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.
Files of 3D mesh models are often large and hence time-consuming to retrieve from a storage device or to download through the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order...
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Files of 3D mesh models are often large and hence time-consuming to retrieve from a storage device or to download through the network. Most 3D viewing applications need to obtain the entire file of a 3D model in order to display the model, even when the user is interested only in a small part, or a low-resolution version, of the model. Therefore, coding that enables multiresolution and ROI (Region Of Interest) transmission of 3D models is desired. In this paper, we propose a coding algorithm of 3D models based on partitioning schemes. The algorithm actually partitions the 3D meshes into some small submeshes according to some geometric criteria (such as curvatures), and then codes each small sub-meshes separately to transmit it progressively to users on demand. The key idea of this paper lies in the mesh partitioning procedure prior to its LOD control, which enables good compression ratio of the mesh data as well as some other good capable properties through network transmission such as ROI coding, view-adaptive transmission, error resilient coding, etc.
We propose a new wavelet compression algorithm based on the rate-distortion optimization for densely sampled triangular meshes. Exploiting the normal remesher of Guskov et al., the proposed algorithm includes a wavele...
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We propose a new wavelet compression algorithm based on the rate-distortion optimization for densely sampled triangular meshes. Exploiting the normal remesher of Guskov et al., the proposed algorithm includes 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 fast bit allocation, we propose a model-based approach, depending on distribution of the wavelet coefficients. Compared to the state-of-the-art methods for normal meshes, our algorithm provides improvements in coding performance, up to +2.5 dB compared to the original zerotree coder. (C) 2005 Elsevier B.V. All rights reserved.
The size of geometric data sets in scientific and industrial applications is constantly increasing. Storing surface or volume meshes in standard uncompressed formats results in large files that are expensive to store ...
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The size of geometric data sets in scientific and industrial applications is constantly increasing. Storing surface or volume meshes in standard uncompressed formats results in large files that are expensive to store and slow to load and transmit. Scientists and engineers often refrain from using mesh compression because currently available schemes modify the mesh data. While connectivity is encoded in a lossless manner, the floating-point coordinates associated with the vertices are quantized onto a uniform integer grid to enable efficient predictive compression. Although a fine enough grid can usually represent the data with sufficient precision, the original floating-point values will change, regardless of grid resolution. In this paper we describe a method for compressing floating-point coordinates with predictive coding in a completely lossless manner. The initial quantization step is omitted and predictions are calculated in floating-point. The predicted and the actual floating-point values are broken up into sign, exponent, and mantissa and their corrections are compressed separately with context-based arithmetic coding. As the quality of the predictions varies with the exponent, we use the exponent to switch between different arithmetic contexts. We report compression results using the popular parallelogram predictor, but our approach will work with any prediction scheme. The achieved bit-rates for lossless floating-point compression nicely complement those resulting from uniformly quantizing with different precisions. (c) 2004 Elsevier Ltd. All rights reserved.
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