## 1 Introduction  
The structured light 3D measurement method is one of the typical 3D measurement methods based on the triangulation principle. Since it has the advantages of no contact, high efficiency, high accuracy, and low cost [1], it is widely applied in many fields, such as 3D measurement, industrial production, quality inspection, and other areas [2; 3; 4; 5; 6; 7; 8; 9; 10]. Among a multitude of techniques based on structured light 3D measurement, stripe-coded structured light plays an essential role due to its high attainable measurement accuracy [11]. In the method, the coded structured light stripe patterns (as shown in Figure 1) are first projected onto the object to be measured by a projector, and then the 3D information of the object is calculated according to the modulated stripe patterns [12]. 3D coordinates are obtained based the locations of stripes, which are located according to the gray distribution of the stripes. However, many factors in the measurement process degenerate the distribution of stripe and reduce the measurement accuracy of the system. For example, when the shape of the object to be measured is complex, the stripe pattern projected by the camera on  
Figure 1: Structured Light Stripe Pattern with Encoded Information. the surface of the object can be severely distorted, and a significant impact on the stripe center extraction is caused. The color of the measured object surface may cause an uneven grayscale distribution of the structured light image [13]. In addition, noise is introduced by the components of the measurement system as well as the measurement environment. The noise can blur the stripes and degrade the image quality. All of the above factors may lead to the degradation of pattern features, generating large errors and resulting in the degradation of the accuracy of the structured light measurement system. In order to improve the accuracy of structured light stripe center extraction, scholars have conducted a lot of related research and achieved many results. In 1998, [PERSON] [14] proposed a method to extract the subpixel location of the center and width of a stripe with high accuracy by analyzing the stripe cross-sections that produce asymmetric degradation. This approach used a linear model for extracting stripe centers, which used first- and second-order directional derivatives to extract the center line of the stripe image and reduced the effect of bias caused by asymmetric degradation. [PERSON] [15] proposed an algorithm combining the threshold iterative extremum method with the weighted gray center of gravity method. The algorithm requires the object to be placed within a range of 400 mm to 700 mm in front of the measurement system. The algorithm is efficient in extraction and has the advantage of being adaptable to the measurement of different objects. Aiming at the problem of low accuracy in locating the center of stripes on highlight surfaces, [PERSON] [16] proposed a stripe center locating method with high robustness. This method uses the grayscale center of gravity method to obtain the coarse center of the stripes. Then, the Sobel operator is used to obtain the normal direction field of the stripes. The center of the stripes in the normal direction is then determined using the grayscale center of gravity method, and the sub-pixel level pixel coordinates are obtained using bilinear interpolation. This can eliminate the interference of flash point noise effectively with strong robustness. [PERSON] [17] eliminated the light bar noise through a two-step convolution process and took a median filter to eliminate the random noise in the background. This method searches for the nearest neighboring points near the normal after identifying the normal equation of each point along the edge of the stripe in a small neighborhood. The sub-pixel coordinates of the stripe center are then extracted using the center of gravity method based on the grayscale values of these points. The simulation results prove the effectiveness of the method.