Journal of Control Engineering and Applied Informatics | |
Lazy Wavelet Simplification using Scale-dependent Dense Geometric Variability Descriptors | |
Mihai-Sorin Stupariu1  Bogdan Dumitrescu2  Teodor Cioaca3  | |
[1] of Bucharest;Politehnica"University "University of Bucharest | |
关键词: Computer Graphics; Graphs; Differential Geometric; Methods; Discriminators; Successive Approximations.; | |
DOI : | |
学科分类:农业科学(综合) | |
来源: Societatea Romana de Automatica si Informatica Tehnica | |
【 摘 要 】
Partitioning geometric data into two sets, one corresponding to high frequencies and the other to low frequencies, is a critical operation in the second generation wavelet multiresolution analysis. From a geometric point of view, a region with high variability within a vertex neighborhood at a certain scale indicates a correlation with a signal having a frequency that dominates at that scale. We thus prospect the abilities of several geometric variability descriptors to robustly identify features. We consider three descriptor families: based on principal component analysis, surface fitting and quadric error metrics. To assess the quality of each descriptor, we employ a lazy wavelet simplification of digitized 3D models since these usually contain noisy geometric structures from which multiple scales of resolutions can be inferred. The difference between a simplified model and the highest resolution representation is measured objectively using averaged local distance functions.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO201902190996274ZK.pdf | 4392KB | download |