Remote sensing technology plays a crucial role in today's world, providing a large amount of data for various fields. Different fields require detailed analysis of different types of product characteristics to meet different needs. Therefore, in-depth understanding of product characteristics is crucial to improve the efficiency of decision making and target management. The existing data comparative analysis methods of product characteristics often only carry out a single dimension comparison, which is not conducive to users to understand the target product characteristics. This paper designs a comparative analysis method of multi-dimensional data based on product characteristics. By obtaining different types of raw data of the target, feature extraction is carried out on each type of raw data respectively to obtain the corresponding feature data of each type, and different feature data are normalized. The normalized feature data is compared and displayed with the current type of known feature data of each known target in the database, and the comparison results of each dimension of the target are obtained.
KEYWORDS: 3D modeling, Data modeling, 3D displays, 3D acquisition, Holography, 3D projection, Holographic displays, Performance modeling, Cameras, Design
Due to the rapid information process, the amount of product characteristic data increases exponentially. In the field of product characteristic display, traditional technology can no longer meet people's needs. In this paper, a three-dimensional projection system based on stereo projection is designed to realize three-dimensional real-time projection of product characteristic data, and the three-dimensional model and product characteristics are superimposed into the holographic scene by constructing a three-dimensional model of the product. Form a display form combining static and dynamic. The representation of product characteristic data is upgraded from a simple plane form to a three-dimensional form combined with a three-dimensional model, and the relationship between product characteristics and target attitude changes is intuitively revealed, significantly improving people's understanding of product characteristic data.
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