Implementation of 3D mesh streaming and compression techniques in NVEs

In this paper, we present a framework that integrates three‐dimensional (3D) mesh streaming and compression techniques and algorithms into our EVE‐II networked virtual environments (NVEs) platform, in order to offer support for large‐scale environments as well as highly complex world geometry. This framework allows the partial and progressive transmission of 3D worlds as well as of separate meshes, achieving reduced waiting times for the end‐user and improved network utilization. We also present a 3D mesh compression method focused on network communication, which is designed to support progressive mesh transmission, offering a fast and effective means of reducing the storage and transmission needs for geometrical data. This method is integrated in the above framework and utilizes prediction to achieve efficient lossy compression of 3D geometry. Copyright © 2006 John Wiley & Sons, Ltd.     

面向移动终端的三角网格逆细分压缩算法

针对移动用户的实时显示需求,提出一种基于逆细分的三角网格压缩算法.通过改进逆Butterfly简化算法,采用逆改版Loop模式,将细密的三角网格简化生成由稀疏的基网格和一系列偏移量组成的渐进网格;然后,通过设计偏移量小波树,将渐进网格进行嵌入式零树编码压缩.实验结果表明:该算法与以往方法相比,在获得较高压缩比的同时,运行速度较快.适用于几何模型的网络渐进传输和在移动终端上的3D图形实时渲染.     

A Parallel Approach to Compression and Decompression of Triangle Meshes using the GPU

Most state‐of‐the‐art compression algorithms use complex connectivity traversal and prediction schemes, which are not efficient enough for online compression of large meshes. In this paper we propose a scalable massively parallel approach for compression and decompression of large triangle meshes using the GPU. Our method traverses the input mesh in a parallel breadth‐first manner and encodes the connectivity data similarly to the well known cut‐border machine. Geometry data is compressed using a local prediction strategy. In contrast to the original cut‐border machine, we can additionally handle triangle meshes with inconsistently oriented faces. Our approach is more than one order of magnitude faster than currently used methods and achieves competitive compression rates.     

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.     

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.     

三角形条带网格模型几何压缩方法研究

三角形条带为三角形网格提供了一种紧凑的表示方法，使快速的绘制和传输三角形网格成为可能，因此对由三角形条带构成的网格压缩进行研究具有重要的意义．本文使用Triangle Fixer方法对三角形条带构成的三维模型拓扑信息进行了压缩，并采用3阶自适应算术编码进一步提高压缩率；同时结合量化、平行四边形顶点坐标预测以及算术编码来实现三角形网格几何信息的压缩，在几何模型质量基本没有损失的情况下，获得了很好的压缩性能．     

Model-Based Power Aware Compression Algorithms for MPEG-4 Virtual Human Animation in Mobile Environments

MPEG-4 body animation parameters (BAP) are used for animation of MPEG-4 compliant virtual human-like characters. Distributed virtual reality applications and networked games on mobile computers require access to locally stored or streamed compressed BAP data. Existing MPEG-4 BAP compression techniques are inefficient for streaming, or storing, BAP data on mobile computers, because: 1) MPEG-4 compressed BAP data entails a significant number of CPU cycles, hence significant, unacceptable power consumption, for the purpose of decompression, 2) the lossy MPEG-4 technique of frame dropping to reduce network throughput during streaming leads to unacceptable animation degradation, and 3) lossy MPEG-4 compression does not exploit structural information in the virtual human model. In this article, we propose two novel algorithms for lossy compression of BAP data, termed as BAP-Indexing and BAP-Sparsing. We demonstrate how an efficient combination of the two algorithms results in a lower network bandwidth requirement and reduced power for data decompression at the client end when compared to MPEG-4 compression. The algorithm exploits the structural information in the virtual human model, thus maintaining visually acceptable quality of the resulting animation upon decompression. Consequently, the hybrid algorithm for BAP data compression is ideal for streaming of motion animation data to power- and network-constrained mobile computers     

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.     

Optimal Packet Loss Protection of Progressively Compressed 3-D Meshes

We consider a state-of-the-art system that uses layered source coding and forward error correction with Reed-Solomon codes to efficiently transmit 3-D meshes over lossy packet networks. Given a transmission bit budget, the performance of this system can be optimized by determining how many layers should be sent, how each layer should be packetized, and how many parity bits should be allocated to each layer such that the expected distortion at the receiver is minimum. The previous solution for this optimization problem uses exhaustive search, which is not feasible when the transmission bit budget is large. We propose instead an exact algorithm that solves this optimization problem in linear time and space. We illustrate the advantages of our approach by providing experimental results for the compressed progressive meshes (CPM) mesh compression technique.     

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.     

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.     

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.     

Fine-Granularity Transmission Distortion Modeling for Video Packet Scheduling Over Mesh Networks

Packet scheduling is a critical component in multi-session video streaming over mesh networks. Different video packets have different levels of contribution to the overall video presentation quality at the receiver side. In this work, we develop a fine-granularity transmission distortion model for the encoder to predict the quality degradation of decoded videos caused by lost video packets. Based on this packet-level transmission distortion model, we propose a content-and-deadline-aware scheduling (CDAS) scheme for multi-session video streaming over multi-hop mesh networks, where content priority, queuing delays, and dynamic network transmission conditions are jointly considered for each video packet. Our extensive experimental results demonstrate that the proposed transmission distortion model and the CDAS scheme significantly improve the performance of multi-session video streaming over mesh networks.     

HFPaC: GPU friendly height field parallel compression

In this paper, we present a novel method for fast lossy or lossless compression and decompression of regular height fields. The method is suitable for SIMD parallel implementation and thus inherently suitable for modern GPU architectures. Lossy compression is achieved by approximating the height field with a set of quadratic Bezier surfaces. In addition, lossless compression is achieved by superimposing the residuals over the lossy approximation. We validated the method’s efficiency through a CUDA implementation of compression and decompression algorithms. The method allows independent decompression of individual data points, as well as progressive decompression. Even in the case of lossy decompression, the decompressed surface is inherently seamless. In comparison with the GPU-oriented state-of-the-art method, the proposed method, combined with a widely available lossless compression method (such as DEFLATE), achieves comparable compression ratios. The method’s efficiency slightly outperforms the state-of-the-art method for very high workloads and considerably for lower workloads.     

Edgebreaker: connectivity compression for triangle meshes

Edgebreaker is a simple scheme for compressing the triangle/vertex incidence graphs (sometimes called connectivity or topology) of three-dimensional triangle meshes. Edgebreaker improves upon the storage required by previously reported schemes, most of which can guarantee only an O(t log(t)) storage cost for the incidence graph of a mesh of t triangles. Edgebreaker requires at most 2t bits for any mesh homeomorphic to a sphere and supports fully general meshes by using additional storage per handle and hole. For large meshes, entropy coding yields less than 1.5 bits per triangle. Edgebreaker's compression and decompression processes perform identical traversals of the mesh from one triangle to an adjacent one. At each stage, compression produces an op-code describing the topological relation between the current triangle and the boundary of the remaining part of the mesh. Decompression uses these op-codes to reconstruct the entire incidence graph. Because Edgebreaker's compression and decompression are independent of the vertex locations, they may be combined with a variety of vertex-compressing techniques that exploit topological information about the mesh to better estimate vertex locations. Edgebreaker may be used to compress the connectivity of an entire mesh bounding a 3D polyhedron or the connectivity of a triangulated surface patch whose boundary need not be encoded. The paper also offers a comparative survey of the rapidly growing field of geometric compression     

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。     

渐进网格及其在移动计算中的应用

在移动计算中,3维图形通常是由几何造型的网格来表示。为了解决移动图形的存储、传输和显示问题,提出了一种基于逆细分的构建渐进网格的算法,给出了渐进网格通过网格传输和在移动终端上渲染3维图形的方法。细密的网格通过逐层地、分批地删除其冗余信息,最后生成由基网格和一系列误差值组成的渐进网格。在算法实施时,将Loop逼近型细分模式作为插值型细分模式进行操作。该算法共分3个关键步骤:网格分裂、奇点预测、网格更新。简化后的渐进网格可以无损还原。实验结果表明,该算法效率高,比以往的方法速度快。     

Memory optimization in FPGA-accelerated scientific codes based on unstructured meshes

This paper approaches the memory bottleneck problem in FPGA-accelerated codes processing unstructured meshes. A methodology to reduce the required memory bandwidth is presented and evaluated, based on the combined application of data sorting, coding and compression techniques. Sorting allows efficient streaming between the memory and the FPGA, improving data locality and avoiding redundant data requests. Coding achieves a compact representation of the mesh connectivity. Differential compression reduces the size of the mesh data. We propose a hardware implementation with low resource requirements, tailored to accelerators based on reconfigurable devices. The combination of techniques reduces the memory traffic of two computational problems down to an average 34% and 19% of their original sizes, respectively.     

多尺度非均匀渐进网格压缩编码研究

在边折叠网格简化算法的基础上,提出一种多尺度非均匀渐进网格简化算法。该算法不仅简化速度快,而且克服了传统算法在简化模型中网格分布均匀、无法突出模型重要特征的不足之处。该算法在模型的折叠、拐角、凹凸等表达特征的地方,采用的三角形网格面积较小、数量较多;而在平坦等不突出模型特征区域的三角形网格面积较大、数量较少。在简化程度较大的情况下仍然能够保持原始网格的几何特征和视觉特征。由于采用了渐进式多尺度的编码方式,再通过流式传输技术,就可以使采用这种算法生成的模型文件高效、方便地以渐进方式在网络上进行传输。