Zerotree Coding of Subdivision Wavelet Coefficients in Dynamic Time-Varying Meshes.

Multi-iteration wavelet zero-tree coding for image compression

In this paper, we present a wavelet-based progressive compression method for 3-D dynamic meshes. Our method exploits the spatial and temporal redundancy. We encode the geometry of base mesh, the wavelet coefficients and the connectivity of each resolution level in order to reduce the spatial redundancy of intra meshes. For inter mesh coding, we encode the differences of geometry of base meshes and of their wavelet coefficients between adjacent frames to reduce the temporal redundancy. Our proposal is based on the wavelet-based multiresolution analysis which uses a perfect reconstruction filter bank and therefore it enables not only progressive representation but also lossless compression. The simulation results demonstrate that the proposed method is applicable to lossy and lossless compression of 3-D dynamic meshes.

We present a new approach to dynamic mesh compression, which combines compression with simplification to achieve improved compression results, a natural support for incremental transmission and level of detail. The algorithm allows fast progressive transmission of dynamic 3D content. Our scheme exploits both temporal and spatial coherency of the input data, and is especially efficient for the case of highly detailed dynamic meshes. The algorithm can be seen as an ultimate extension of the clustering and local coordinate frame (LCF)‐based approaches, where each vertex is expressed within its own specific coordinate system. The presented results show that we have achieved better compression efficiency compared to the state of the art methods. Copyright © 2008 John Wiley & Sons, Ltd.

ISO/IEC JTC1 SC29, also called MPEG, has been working on a compression standard for dynamic meshes since couple of year now and it has released a Call for Proposals (CfP) for Dynamic Mesh Coding in October 2021. One of the goals of the future standard is to utilize the Visual Volumetric Video-based Coding (V3C) framework, defined in ISO/IEC 23090-5, that is already used for dynamic point cloud compression and volumetric video. In this paper, the authors described their vision of how dynamic mesh compression could be achieved, which corresponds to their technical response to the CfP. The presented objective and subjective results will show that the proposed solution outperforms the anchor in terms of objective metrics and subjective perceived visual quality for low bit rate use cases.

The recent widespread of processing and transmitting 3D model in various fields such as computer graphics, animations and visualization calls an essential need for efficient geometry mesh compression technique that became more crucial. This paper explores a progressive compression technique for 3D normal meshes geometry by utilizing one of competitive learning methods. The introduced technique is based on multi-resolution decomposition which was obtained by wavelet transformation. Then the coefficients are quantized by neural gas algorithm as a vector quantizer which improves the visual quality of the reconstructed geometry mesh. Our experiments show that the explored technique out performs the state-of-art techniques in Terms of visual quality of compressed meshes.

Mesh-based animation, usually represented as dynamic meshes with fixed connectivity, is becoming more and more prevalent in movies, games and other graphics applications nowadays, and there is a growing need to compactly store and rapidly transmit these meshes for practical use, especially for those with high-quality geometric details. In this paper, we explore a novel key-frame based dynamic mesh compression method, wherein we apply pose-similarity with spectral techniques to define piece-wise manifold harmonic bases to reduce spatial-temporal redundancy. We first partition the sequence into several clusters with similar poses, and then decompose the meshes in each cluster into primary poses and geometric details using the manifold harmonic bases derived from the extracted key-frame in that cluster. The primary poses can be characterized as linear combinations of manifold harmonic bases, and the geometric details can be recovered by deformation transfer technique. Thus, we only need a small number of key-frames and a few coefficients for compressing dynamic meshes, which saves a significant amount of storage comparing to traditional methods in which bases are stored explicitly. Furthermore, we apply a second-order linear prediction coding to the harmonic coefficients to further reduce the temporal redundancy. Our extensive experiments and evaluations on various datasets have manifested that our novel method could obtain a high compression ratio while preserving high-fidelity geometry details and guaranteeing limited human perceived distortion rate simultaneously.

3D triangle meshes are a common form for representing the geometry of static and dynamic 3D objects. They are employed already in many areas, e.g. e-commerce, video games, online museums, CGI or 3D animated films, etc. Static triangle meshes represent only a piecewise linear approximation of complex 3D objects. As a consequence the approximation error can be unacceptably high unless the number of triangles is sufficiently large. On the other hand a large number of triangles makes these meshes cumbersome to handle and expensive to store or to transmit. Consequently, there exists a demand for techniques for efficient compression of static and dynamic 3D meshes. In this article we start with basics on 3D meshes. Thereafter, we explain the key ideas behind different mesh compression approaches for static and dynamic 3D meshes, and highlight their similarities and differences. Finally, we introduce the upcoming MPEG standard for compression of dynamic 3D meshes, which is referred to as FAMC (Frame-based Animated Mesh Compression), and show comparative compression results.

The Video-based Dynamic Mesh Coding (V-DMC) standard is a cutting-edge technology for compressing dynamic mesh data. V-DMC realizes parallel and partial decoding by introducing submesh frameworks, where dynamic meshes are segmented and processed independently. However, V-DMC may introduce submesh boundary errors due to misalignments in the existence or coordinates of vertices. Here, submesh boundary errors are defined as the misalignments of vertex coordinates and connectivity between adjacent submesh boundaries. These errors can create holes, thereby degrading both the objective and subjective quality of the decoded dynamic meshes. To minimize boundary errors and enhance coding performance, we propose a two-stage boundary error correction method integrated into V-DMC's preprocessing and encoding/decoding processes. Specifically, the first stage involves rearranging the preprocessing order to minimize boundary errors, and the second stage fills holes using boundary information. Experimental results demonstrate that the proposed method effectively minimizes boundary errors in V-DMC decoded meshes, significantly improving both objective and subjective quality compared to the V-DMC reference software. The D1 BD-Rate gain, indicative of coding efficiency, averages -16.3% for all intra and -25.9% for random access conditions.

Morphing is a technique that smoothly transforms a shape onto another. In this paper, we present a method for morphing of two dynamic meshes: mesh sequences representing the keyframes of animated shapes over time. The pipeline of the proposed method comprises two main stages: template-based cross-parameterization and dynamic mesh interpolation. In the cross-parameterization stage, we use a variation of least-squares (LS) meshes to provide a coarse approximation of the geometry of the source mesh onto the target mesh. In our method, the possible candidates for initial control points of the LS-mesh are detected using an approach based on the Heat Kernel Signature (HKS). Then, an iterative process of fine fitting adds new constraints in the LS-mesh processing. The cross-parameterization is performed just once for any two frames in order to establish a full correspondence between vertices of the source and target meshes. Next, we use such a correspondence in the dynamic mesh interpolation stage to produce the morphing results. The method is entirely mesh-based and does not demand the generation of skeletons, mesh segmentation or the use of any additional control structures. Moreover, it does not require the two input meshes to share the same number of vertices or triangles, or to have the same connectivity. The provided results show the robustness and effectiveness of our method.

Progressive compression of manifold polygon meshes

Rate-distortion-optimized predictive compression of dynamic 3D mesh sequences

The simulation of the store separation using the automatic coupling of dynamic equations with flow aerodynamics is addressed. The precision and cost (calculation time) were considered as comparators. The method used in the present research decreased the calculation cost while limiting the solution error within a specific and tolerable interval. The methods applied to model the aerodynamic forces are time-dependent dynamic meshes and quasi-static methods. In the time-dependent method, a dynamic unstructured tetrahedral mesh approach using combination of spring-based smoothing and local remeshing is employed in respect of bodies motion with an implicit, second-order upwind accurate 3d Euler solver. In this method, a 6dof dynamic code is coupled with the flow solver to update the store trajectory information. In the quasi-static method, a 3-D implicit, steady state Euler solver is automatically integrated with a grid generation software and a 6dof dynamic code. Although the time-dependent method is more precise and reliable, it is not proper and appropriate for the initial design of the separation system due to its high cost. The quasi-static solution is very fast, but unable to simulate realistically because of not satisfying the problem conditions due to solution divergence as the store speed increases. The method used in the present research decreased the calculation cost while limiting the solution error within a specific and tolerable interval. In this way, the time step can be enlarged, the solution can be carried out with a few calculation points, and the solution can have considerably more speed with a limited error magnitude. Simulation of the store separation using the automatic coupling of dynamic equations with flow aerodynamics with the new Low-Cost method is the innovative aspect of this paper. To validate the solution method, the transonic store separation was simulated that agreed well with the wind tunnel test outcomes.

Zerotree coding based on the wavelet transform is a very effective image compression technique. Since it exploits the dyadic multiresolution characteristic of wavelet transform, however, it is not easy to adapt it to the wavelet packet decomposition, which provides better energy compaction than the wavelet transform, but does not have dyadic multiresolution structures. In this paper appropriate coefficient rearrangement in the wavelet packet transform domain is considered for efficient zerotree coding. The proposed coding scheme is proved to be simple and superior to the existing zerotree coding schemes.

We introduce an efficient algorithm for real-time compression of temporally consistent dynamic 3D meshes. The algorithm uses mesh connectivity to determine the order of compression of vertex locations within a frame. Compression is performed in a frame to frame fashion using only the last decoded frame and the partly decoded current frame for prediction. Following the predictive coding paradigm, local temporal and local spatial dependencies between vertex locations are exploited. In this framework we present a novel angle preserving predictor and evaluate its performance against other state of the art predictors. It is shown that the proposed algorithm improves up to 25% upon the current state of the art for compression of temporally consistent dynamic 3D meshes.

Data compression is an area of a major interest in computational terms due to the issues on storage and transmission. Particulary, mesh compression has wide usage due to the increase of its application in games and three-dimensional modeling. In recent years, a new theory of acquisition and reconstruction of signals was developed, based on the concept of sparsity and in the minimization of the L 1 norm and the incoherency of the signal, called Compressive Sensing (CS). This theory has some remarkable features, such as random sampling and reconstruction by minimization, in a way that the signal acquisition is done by considering only its significant coefficients. Any object that can be interpreted as a sparse sign allows its use. Thus, representing an object sparsely (sounds, images), you can apply the technique of CS. This work explores the viability of CS theory on mesh compression, so that it is possible a representative and compressive sensing on the mesh geometry. In the performed experiments, different parameters and L 1 Norm minimization strategies were used. The results show that CS can be used as a mesh geometry compression strategy.