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基于多尺度流形学习的三维计算机图形自适应网格压缩Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning |
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| 课程网址: | http://videolectures.net/icml07_mahadevan_amc/ |
| 主讲教师: | Sridhar Mahadevan |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2007-06-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper investigates compression of 3D ob jects in computer graphics using manifold learning. Spectral compression uses the eigenvectors of the graph Laplacian of an object's topology to adaptively compress 3D objects. 3D compression is a challenging application domain: ob ject models can have > 105 vertices, and reliably computing the basis functions on large graphs is numerically challenging. In this paper, we introduce a novel multiscale manifold learning approach to 3D mesh compression using diffusion wavelets, a general extension of wavelets to graphs with arbitrary topology. Unlike the "global" nature of Laplacian bases, diffusion wavelet bases are compact, and multiscale in nature. We decompose large graphs using a fast graph partitioning method, and combine local multiscale wavelet bases computed on each subgraph. We present results showing that multiscale diffusion wavelets bases are superior to the Laplacian bases for adaptive compression of large 3D ob jects. |
| 关 键 词: | 流形学习; 计算机图形学; 3D对象 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 108 |