| Summary: | 碩士 === 中原大學 === 資訊工程研究所 === 94 === In recent years, the research on the compression of 3D animation sequence has become more important in computer graphics . Animation can be discussed in two parts, temporal and spatial parts .Depend on temporal domain, animation sequences was combined with several single static meshes over time , which was called frames. In each frame, a single 3D mesh has different variation in spatial domain. However, a single 3D static model requires large storage space for vertex information and adjacency relationship of triangles. For life-like soft body animations of the 3D data, because the vertex positions change over time, the storage space and the amount of calculation required would be even more voluminous, but the connectivity of adjacency triangles are the same in each frames. Hence, the reduction concept emerges in the hope of reducing the required storage space and keeps within an acceptable error range.
This paper concerns with the use of the affine transformation matrix in working with Principal Component Analysis (PCA) to compress the data of 3D animation models. Satisfactory results were achieved for the common 3D models by PCA because it can simplify several related variables to a few independent main factors in addition to make the animation identical to the original by linear combination. However, it is more suitable for animation models for original semi-movement. If the original animation is integrated with the transformation movements such as translation, rotation, and scaling, the animation model for transformation movement will have a greater distortion in the case of the same base vector as compared with the original animation model for semi-movement.
In this paper , the first is to extract the model movement characteristics using the affine transformation matrix and then to compress 3D animation by PCA. The affine transformation matrix can record the changes in geometric transformation by using 4 * 4 matrixes. The transformed model can eliminate the influences of geometric transformation with the animation model normalized to limited space. Then, by using PCA, the most suitable base vector (variance) can be selected more precisely.
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