Progressive Mesh Compression Research Articles

Progressive Compression of Triangle Meshes

This paper details the first publicly available implementation of the progressive mesh compression algorithm described in the paper entitled "Compressed Progressive Meshes" [R. Pajarola and J. Rossignac, IEEE Transactions on Visualization and Computer Graphics, 6 (2000), pp. 79-93]. Our implementation is generic, modular, and includes several improvements in the stopping criteria and final encoding. Given an input 2-manifold triangle mesh, an iterative simplification is performed, involving batches of edge collapse operations guided by an error metric. During this compression step, all the information necessary for the reconstruction (at the decompression step) is recorded and compressed using several key features: geometric quantization, prediction, and spanning tree encoding. Our implementation allowed us to carry out an experimental comparison of several settings for the key parameters of the algorithm: the local error metric, the position type of the resulting vertex (after collapse), and the geometric predictor.

A PDE patch-based spectral method for progressive mesh compression and mesh denoising

The development of the patchwise partial differential equation (PDE) framework a few years ago has paved the way for the PDE method to be used in mesh signal processing. In this paper, we, for the ...

A PDE patch-based spectral method for progressive mesh compression and mesh denoising

The development of the patchwise partial differential equation (PDE) framework a few years ago has paved the way for the PDE method to be used in mesh signal processing. In this paper, we, for the first time, extend the use of the PDE method to progressive mesh compression and mesh denoising. We, meanwhile, upgrade the existing patchwise PDE method in patch merging, mesh partitioning, and boundary extraction to accommodate mesh signal processing. In our new method, an arbitrary mesh model is partitioned into patches, each of which can be represented by a small set of coefficients of its PDE spectral solution. Since low-frequency components contribute more to the reconstructed mesh than high-frequency ones, we can achieve progressive mesh compression and mesh denoising by manipulating the frequency terms of the PDE solution. Experimental results demonstrate the feasibility of our method in both progressive mesh compression and mesh denoising.

Triangle mesh compression along the Hamiltonian cycle

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.

3D Model compression using Connectivity-Guided Adaptive Wavelet Transform built into 2D SPIHT

Connectivity-Guided Adaptive Wavelet Transform based mesh compression framework is proposed. The transformation uses the connectivity information of the 3D model to exploit the inter-pixel correlations. Orthographic projection is used for converting the 3D mesh into a 2D image-like representation. The proposed conversion method does not change the connectivity among the vertices of the 3D model. There is a correlation between the pixels of the composed image due to the connectivity of the 3D mesh. The proposed wavelet transform uses an adaptive predictor that exploits the connectivity information of the 3D model. Known image compression tools cannot take advantage of the correlations between the samples. The wavelet transformed data is then encoded using a zero-tree wavelet based method. Since the encoder creates a hierarchical bitstream, the proposed technique is a progressive mesh compression technique. Experimental results show that the proposed method has a better rate distortion performance than MPEG-3DGC/MPEG-4 mesh coder.

The progressive mesh compression based on meaningful segmentation

Nowadays, both mesh meaningful segmentation (also called shape decomposition) and progressive compression are fundamental important problems, and some compression algorithms have been developed with the help of patch-type segmentation. However, little attention has been paid to the effective combination of mesh compression and meaningful segmentation. In this paper, to accomplish both adaptive selective accessibility and a reasonable compression ratio, we break down the original mesh into meaningful parts and encode each part by an efficient compression algorithm. In our method, the segmentation of a model is obtained by a new feature-based decomposition algorithm, which makes use of the salient feature contours to parse the object. Moreover, the progressive compression is an improved degree-driven method, which adapts a multi-granularity quantization method in geometry encoding to obtain a higher compression ratio. We provide evidence that the proposed combination can be beneficial in many applications, such as view-dependent rendering and streaming of large meshes in a compressed form.