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An Approach of Adaptive Acquisition and Modeling for Free-form Surface with Structured-Light Vision Sensor

机译:基于结构光视觉传感器的自由曲面自适应采集与建模方法

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Rapid and high precision data acquisition methodology from coordinate components with free-form surface and geometrical model can be implemented widely. Typical application covers part localization, automatic calibration and reverse engineering. Integrated structured light vision sensor with CMM (Coordinate Measurement Machine) enhances the high-precision coordinate measurement capability. In this paper a curvature-based adaptive sampling approach and the evaluating index for the sampling precision are presented. The matching and subdividing algorithm for generating matrix-type mesh data from sample points is described. The methodology to register and merge the measured data from multiple viewpoints to model the free-form surface is also presented. Based on the given initial coordinate rotation matrix R and transformation vector T, the different viewpoints can be translated into a unique frame of reference. By introducing special coordinate of 3D space, the registered data is dividued into mesh, which cover the whole surface of object. An application example for shoe modeling is described to illustrate and advantages.
机译:从具有自由曲面和几何模型的坐标分量中快速,高精度地获取数据的方法可以得到广泛应用。典型应用包括零件定位,自动校准和逆向工程。带有CMM(坐标测量机)的集成结构光视觉传感器增强了高精度坐标测量能力。本文提出了一种基于曲率的自适应采样方法,并给出了采样精度的评价指标。描述了用于从样本点生成矩阵类型网格数据的匹配和细分算法。还介绍了从多个视点注册和合并测量数据以对自由曲面进行建模的方法。基于给定的初始坐标旋转矩阵R和变换矢量T,可以将不同的视点转换为唯一的参考系。通过引入3D空间的特殊坐标,将注册数据划分为网格,覆盖对象的整个表面。描述了鞋建模的一个应用示例,以说明其优点。

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