The total lateral runout of the direct driver (DD) motor directly affects the positioning accuracy of DD. In real-time production, face runout is measured using dial indicators because of the complex measurement process for total face runout. This method can easily damage the surface of the measured object and is associated with inefficiency and low precision. To address this issue, a method to measure the DD motor end face runout based on point cloud driving is proposed. In this method, the point cloud on the DD motor surface is obtained using a linear laser sensor that incorporates laser triangulation to measure the distance of the object. The Z-axis measurement accuracy is 1.8-3.0 μm, and the repetition accuracy is $\mathrm{0.4}\mathrm{\mu }\mathrm{m}$. Then, the point cloud is first compressed using a uniform down sampling algorithm and then segmented using a hybrid segmentation algorithm based on curvature and density. The segmentation of the compressed point cloud helps in obtaining the point cloud of the DD motor working face. The outliers in the point cloud of the working face are then analyzed and eliminated using an algorithm based on the Pauta criterion. Finally, the random sampling consistency algorithm is used to fit the point cloud plane to obtain the point cloud plane equation, and the plane is used as the reference to calculate the total end face runout of the DD motor. Our experimental results show that the measurement result of the proposed method exhibits about 12% error at the micrometer level compared to the reference result, indicating the effectiveness of the proposed method and confirming that it also meets the industrial precision requirements. In addition, the DD motor end face runout measurement software is developed based on the point cloud library, Qt, and Visual Studio platforms. The developed software realizes data display, point cloud processing, one-click measurement, data management, and other functions.