Abstract: Axial data is often visualized in a pseudo color manner on 3D pipeline models,and the color mapping function has a decisive impact on the visualization effect.Although there are now many color mapping functions that have good effects on data that conform to common distribution characteristics,it is still difficult to achieve ideal results on data with special distributions.Therefore,people are studying color mapping functions for various special distribution data.If the axial data of a three-dimensional pipeline is composed of several segments and presents a distribution characteristic of “small differences within segments and large differences between segments”,then common mapping functions are difficult to simultaneously represent subtle differences within segments and significant differences between segments,which affects the visualization effect.To address this issue,this article proposes a mapping function optimization method based on control points to improve the visualization effect of segmented data.Experimental analysis first verifies the effectiveness of the method using synthetic data; it also verifies the effectiveness of the method using radiation data from the cooling tubes of fusion reactors,and both types of data confirm the effectiveness of this method.

Key words: Scientific visualization, Color mapping, Control points, Visualization optimization