首页> 外文会议>SIGGRAPH conference on computer graphics >Progressive Compression for Lossless Transmission of Triangle Meshes
【24h】

Progressive Compression for Lossless Transmission of Triangle Meshes

机译:Triangle网格无损传输的渐进压缩

获取原文

摘要

Lossless transmission of 3D meshes is a very challenging and timely problem for many applications, ranging from collaborative design to engineering. Additionally, frequent delays in transmissions call for progressive transmission in order for the end user to receive useful successive refinements of the final mesh. In this paper, we present a novel, fully progressive encoding approach for lossless transmission of triangle meshes with a very fine granularity. A new valence-driven decimating conquest, combined with patch tiling and an original strategic retriangulation is used to maintain the regularity of valence. We demonstrate that this technique leads to good mesh quality, near-optimal connectivity encoding, and therefore a good rate-distortion ratio throughout the transmission. We also improve upon previous lossless geometry encoding by decorrelating the normal and tangential components of the surface. For typical meshes, our method compresses connectivity down to less than 3.7 bits per vertex, 40% better in average than the best methods previously reported; we further reduce the usual geometry bit rates by 20% in average by exploiting the smoothness of meshes. Concretely, our technique can reduce an ascii VRML 3D model down to 1.7% of its size for a 10-bit quantization (2.3% for a 12-bit quantization) while providing a very progressive reconstruction.
机译:对于许多应用来说,3D网格的无损传输是一个非常具有挑战性的,并且是对工程的协同设计的极具挑战性和及时的问题。另外,传输频繁延迟呼叫逐行传输,以便最终用户接收最终网格的有用连续改进。在本文中,我们提出了一种新颖,完全逐步的编码方法,用于具有非常细粒度的三角网格的无损传输。一种新的价驾驶抽取征服,与补丁平铺和原始的​​战略检定相结合,用于保持价值的价值。我们证明该技术导致良好的网格质量,近最佳连接编码,因此在整个传输过程中良好的速率失真率。我们还通过去相关性表面的正常和切向组分来提高先前的无损几何形状。对于典型网格,我们的方法压缩到每周顶点的连接到小于3.7位,平均优于先前报告的最佳方法40%;我们通过利用网格的平滑度进一步将通常的几何比特率降低20%。具体地,我们的技术可以将ASCII VRML 3D模型降低到其尺寸的1.7%,以获得10位量化(12位量化的2.3%),同时提供非常渐进的重建。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号