Cross-scale measurement techniques are pivotal for seamless coordination between robotic macro and micromanipulation. While computer microvision offers non-contact and multi-degree-of-freedom capabilities, its limitations in measurement range and narrow field-of-view (FOV) prove unsatisfactory for robotic macro-micro manipulation. This study presents a marker-assisted microvision-based method to minimize errors during measurement range extension and ensure precise cross-scale motion measurement for robotic macro-micro manipulation. The microvision-based system utilizes the microstructural pattern designed for global map construction. Additionally, it employs a learning-based approach to efficiently estimate the FOV position for macro-motion measurement, followed by the integration of micro-motion tracking within the FOV for precise micro-motion measurements. Experimental investigations showcase a measurement accuracy of 9 μm in an extended range of 3.11 mm for macro-motion estimation and 219 nm for micro-motion measurement, which highlight the microvision-based method in achieving effective cross-scale motion measurement, potentially paving the way for the automation of robotic macro-micro manipulation.