关于三维网格压缩和渐进传输问题的几点探讨

通过对渐进网格（PM）和压缩渐进网格（CPM）技术的探讨,提出了在采用线性插值的方法分批传输压缩数据的同时,动态细化3D模型的几何网格。相应地,对CPM算法进行了简化,以减少解压时的开销,在CPM的基础上使渐进细化网格有更好的显示效果。     

R-D optimized progressive compression of 3D meshes using prioritized gate selection and curvature prediction

A rate-distortion (R-D) optimized progressive coding algorithm for three-dimensional (3D) meshes is proposed in this work. We propose the prioritized gate selection and the curvature prediction to improve the connectivity and geometry compression performance, respectively. Furthermore, based on the bit plane coding, we develop a progressive transmission method, which improves the qualities of intermediate meshes as well as that of the fully reconstructed mesh, and extend it to the view-dependent transmission method. Experiments on various 3D mesh models show that the proposed algorithm provides significantly better compression performance than the conventional algorithms, while supporting progressive reconstruction efficiently.     

一种快速的可逆累进网格算法

在分析已有累进网格生成算法的基础上,构造了一种新的网格简化信息记录表示法,并提出一种基于“边折叠”网格简化方法的累进网格生成算法。此算法不仅消除了累进网格技术中的二义性,而且能够较大地提高累进网格的运算速度。     

一种3维模型的网格盲水印算法

3维模型的数字水印是数字水印研究的热点之一,鲁棒性、嵌入可读性水印和盲检测是3维模型数字水印的难点所在。从增强3维模型数字水印的鲁棒性出发,提出一种基于局部几何空间的3维模型数字水印算法,它以模型三角网格顶点在其一环邻居顶点为所确定的局部几何空间;通过改变顶点在局部几何空间中的位置来嵌入水印,并以调整其间的夹角来嵌入水印序列的索引,而调整顶点与圆心的距离来嵌入较高比特的二进制数值。本文算法在水印检测时,无需原始模型,且可抵抗平移、旋转、均匀缩放、顶点乱序、剪切及网格简化等攻击。实验结果表明, 该水印算法具有很好的鲁棒性、可读水印的不可感知性和盲水印检测的优势。     

Combined compression and simplification of dynamic 3D 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.     

Fast accessing Web3D contents using lightweight progressive meshes

Accessing Web3D contents is relatively slow through Internet under limited bandwidth. Preprocessing of 3D models can certainly alleviate the problem, such as 3D compression and progressive meshes (PM). But none of them considers the similarity between components of a 3D model, so that we could take advantage of this to further improve the efficiency. This paper proposes a similarity‐aware data reduction method together with PM, called lightweight progressive meshes (LPM). LPM aims to excavate similar components in a 3D model, generates PM representation of each component left after removing redundant components, and organizes all the processed data using a structure called lightweight scene graph. The proposed LPM possesses four significant advantages. First, it can minimize the file size of 3D model dramatically without almost any precision loss. Because of this, minimal data is delivered. Second, PM enables the delivery to be progressive, so called streaming. Third, when rendering at client side, due to lightweight scene graph, decompression is not necessary and instanced rendering is fully exerted. Fourth, it is extremely efficient and effective under very limited bandwidth, especially when delivering large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state‐of‐the‐art in accessing Web3D contents. Copyright © 2015 John Wiley & Sons, Ltd.     

Wavelet-based progressive compression scheme for triangle meshes: wavemesh

We propose a new lossy to lossless progressive compression scheme for triangular meshes, based on a wavelet multiresolution theory for irregular 3D meshes. Although remeshing techniques obtain better compression ratios for geometric compression, this approach can be very effective when one wants to keep the connectivity and geometry of the processed mesh completely unchanged. The simplification is based on the solving of an inverse problem. Optimization of both the connectivity and geometry of the processed mesh improves the approximation quality and the compression ratio of the scheme at each resolution level. We show why this algorithm provides an efficient means of compression for both connectivity and geometry of 3D meshes and it is illustrated by experimental results on various sets of reference meshes, where our algorithm performs better than previously published approaches for both lossless and progressive compression.     

Fragile watermarking for authenticating 3-D polygonal meshes

Designing a powerful fragile watermarking technique for authenticating three-dimensional (3-D) polygonal meshes is a very difficult task. Yeo and Yeung were first to propose a fragile watermarking method to perform authentication of 3-D polygonal meshes. Although their method can authenticate the integrity of 3-D polygonal meshes, it cannot be used for localization of changes. In addition, it is unable to distinguish malicious attacks from incidental data processings. In this paper, we trade off the causality problem in Yeo and Yeung's method for a new fragile watermarking scheme. The proposed scheme can not only achieve localization of malicious modifications in visual inspection, but also is immune to certain incidental data processings (such as quantization of vertex coordinates and vertex reordering). During the process of watermark embedding, a local mesh parameterization approach is employed to perturb the coordinates of invalid vertices while cautiously maintaining the visual appearance of the original model. Since the proposed embedding method is independent of the order of vertices, the hidden watermark is immune to some attacks, such as vertex reordering. In addition, the proposed method can be used to perform region-based tampering detection. The experimental results have shown that the proposed fragile watermarking scheme is indeed powerful.     

基于单向预测误差扩展的三维医学图像可逆水印算法

对于可逆水印技术在三维医学图像中的应用问题，提出一种基于单向预测误差扩展的三维医学图像可逆水印算法。首先根据像素间的三维梯度变化预测像素从而得到预测误差；然后结合磁共振成像生成的三维医学图像的特征，采用单向直方图位移与预测误差扩展相结合的方法将外部信息嵌入至三维医学图像；最后，重新预测像素，提取外部信息，恢复原始三维图像。实验结果表明，在MR-head和MR-chest测试数据体上，与二维梯度预测相比，所提算法预测误差的平均绝对偏差分别降低1.09和1.40，每个像素的最大嵌入容量分别增加0.0456比特和0.1291比特，从而能够更准确地预测像素值，嵌入更多的外部信息。该算法可应用于对三维医学图像的篡改检测以及患者隐私保护。     

Rate-distortion optimization for progressive compression of 3D mesh with color attributes

We propose a new lossless progressive compression algorithm based on rate-distortion optimization for meshes with color attributes; the quantization precision of both the geometry and the color information is adapted to each intermediate mesh during the encoding/decoding process. This quantization precision can either be optimally determined with the use of a mesh distortion measure or quasi-optimally decided based on an analysis of the mesh complexity in order to reduce the calculation time. Furthermore, we propose a new metric which estimates the geometry and color importance of each vertex during the simplification in order to faithfully preserve the feature elements. Experimental results show that our method outperforms the state-of-the-art algorithm for colored meshes and competes with the most efficient algorithms for non-colored meshes.     

Geometry-based watermarking of 3D models

This article addresses the fundamentals of geometry-based watermarking. It presents a watermarking algorithm that modifies normal distribution to invisibly store information in the model's geometry     

Joint encryption and compression of 3D images based on tensor compressive sensing with non-autonomous 3D chaotic system

Existing techniques for the simultaneous encryption and compression of three-dimensional (3D) image sequences (e.g., video sequences, medical image sequences) may come with sufficient decryption accuracy or compression ratio, but do not inherently have both; the relationship between them is generally ignored because the images of a sequence are handled individually. To address this problem, we designed Tensor Compressive Sensing (TCS) to simultaneously encrypt and compress a 3D sequence as a tensor rather than several 2D images. To further enhance security, a non-autonomous Lorenz system is constructed to control the three measurement matrices of TCS. The proposed method preserves the intrinsic structure of tensor-based 3D image sequences and achieves a favorable balance of compression ratio, decryption accuracy, and security. Numerical simulation results verify the validity and the reliability of the TCS scheme.     

Streaming compression of hexahedral meshes

We describe a method for streaming compression of hexahedral meshes. Given an interleaved stream of vertices and hexahedra our coder incrementally compresses the mesh in the presented order. Our coder is extremely memory efficient when the input stream also documents when vertices are referenced for the last time (i.e. when it contains topological finalization tags). Our coder then continuously releases and reuses data structures that no longer contribute to compressing the remainder of the stream. This means in practice that our coder has only a small fraction of the whole mesh in memory at any time. We can therefore compress very large meshes—even meshes that do not fit in memory. Compared to traditional, non-streaming approaches that load the entire mesh and globally reorder it during compression, our algorithm trades a less compact compressed representation for significant gains in speed, memory, and I/O efficiency. For example, on the 456k hexahedra “blade” mesh, our coder is twice as fast and uses 88 times less memory (only 3.1 MB) with the compressed file increasing about 3% in size. We also present the first scheme for predictive compression of properties associated with hexahedral cells.     

A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets

This paper presents a novel and efficient diagnostically lossless compression for 3D medical image sets. This compression scheme provides the 3D medical image sets with a progressive transmission capability. An automated filter-and-threshold based preprocessing technique is used to remove noise outside the diagnostic region. Then a wavelet decomposition feature vector based approach is applied to determine the reference image for the entire 3D medical image set. The selected reference image contains the most discernible anatomical structures within a relative large diagnostic region. It is progressively encoded by a lossless embedded zerotree wavelet method so the validity of an entire set can be determined early. This preprocessing technique is followed by an optimal predictor plus a 1st-level integer wavelet transform to de-correlate the 3D medical image set. Run-length and arithmetic coding are used to further remove coding redundancy. This diagnostically lossless compression method achieves an average compression of 2.1038, 2.4292, and 1.6826 bits per pixel for three types of 3D magnetic resonance image sets. The integrated progressive transmission capability degrades the compression performance by an average of 7.25%, 6.60%, and 4.49% for the above three types. Moreover, our compression without and with progressive transmission achieves better compression than the state-of-the-art.     

Conformal spherical representation of 3D genus-zero meshes

This paper describes an approach of representing 3D shape by using a set of invariant spherical harmonic (SH) coefficients after conformal mapping. Specifically, a genus-zero 3D mesh object is first conformally mapped onto the unit sphere by using a modified discrete conformal mapping, where the modification is based on Möbius factorization and aims at obtaining a canonical conformal mapping. Then a SH analysis is applied to the resulting conformal spherical mesh. The obtained SH coefficients are further made invariant to translation and rotation, while at the same time retain the completeness, thanks to which the original shape information has been faithfully preserved.     

Hiding depth information in compressed 2D image/video using reversible watermarking

In this paper, a novel joint coding scheme is proposed for 3D media content including stereo images and multiview-plus-depth (MVD) video for the purpose of depth information hiding. The depth information is an image or image channel which reveals the distance of scene objects’ surfaces from a viewpoint. With the concern of copyright protection, access control and coding efficiency for 3D content, we propose to hide the depth information into the texture image/video by a reversible watermarking algorithm called Quantized DCT Expansion (QDCTE). Considering the crucial importance of depth information for depth-image-based rendering (DIBR), full resolution depth image/video is compressed and embedded into the texture image/video, and it can be extracted without extra quality degradation other than compression itself. The reversibility of the proposed algorithm guarantees that texture image/video quality will not suffer from the watermarking process even if high payload (i.e. depth information) is embedded into the cover image/video. In order to control the size increase of watermarked image/video, the embedding function is carefully selected and the entropy coding process is also customized according to watermarking strength. Huffman and content-adaptive variable-length coding (CAVLC), which are respectively used for JPEG image and H.264 video entropy encoding, are analyzed and customized. After depth information embedding, we propose a new method to update the entropy codeword table with high efficiency and low computational complexity according to watermark embedding strength. By using our proposed coding scheme, the depth information can be hidden into the compressed texture image/video with little bitstream size overhead while the quality degradation of original cover image/video from watermarking can be completely removed at the receiver side.     

Hierarchical Laplacian-based compression of triangle meshes

In this paper, we present an algorithm for efficient encoding of triangle meshes. The algorithm preserves the local relations between vertices by encoding their Laplacian coordinates, while at the same time, it uses a hierarchy of additional vertex constraints that provides global rigidity and low absolute error, even for large meshes. Our scheme outperforms traversal based as well as Laplacian-based compression schemes in terms of both absolute and perceived distortion at a given data rate.     

Progressive compression of arbitrary textured meshes

In this paper, we present a progressive compression algorithm for textured surface meshes, which is able to handle polygonal non‐manifold meshes as well as discontinuities in the texture mapping. Our method applies iterative batched simplifications, which create high quality levels of detail by preserving both the geometry and the texture mapping. The main features of our algorithm are (1) generic edge collapse and vertex split operators suited for polygonal non‐manifold meshes with arbitrary texture seam configurations, and (2) novel geometry‐driven prediction schemes and entropy reduction techniques for efficient encoding of connectivity and texture mapping. To our knowledge, our method is the first progressive algorithm to handle polygonal non‐manifold models. For geometry and connectivity encoding of triangular manifolds and non‐manifolds, our method is competitive with state‐of‐the‐art and even better at low/medium bitrates. Moreover, our method allows progressive encoding of texture coordinates with texture seams; it outperforms state‐of‐the‐art approaches for texture coordinate encoding. We also present a bit‐allocation framework which multiplexes mesh and texture refinement data using a perceptually‐based image metric, in order to optimize the quality of levels of detail.     

Partitioning 3D surface meshes using watershed segmentation

This paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design