## 1 Introduction
Stripe extraction is the key factor for fixed mode structured-light equipment to improve measurement accuracy, because the calibration and coding processes have been fixed. In Reference [8], [PERSON] et al. proposed an adaptive stripe intensity method to improve SNR by adjusting the width of stripe according to the intensity of sunlight, which does not increase the extra power consumption, but reduces the scanning efficiency. In Reference [11], [PERSON] et al. analyzed the stripe collected by a camera on direct and indirect light projection paths, and the ambient noise was directly ignored, but the optical simulating model is too complicated. In Reference [12], [PERSON] proposedan unbiased detection method based on Hessian matrix, which can locate the curve points in sub-pixel accuracy and has strong anti-interference ability, but it relies heavily on segmentation results of stripe area. In Reference [13], [PERSON] et al. segmented foreground information using center of  
Figure 1: Snow sculpture artwork. gravity method.Then the relative motion of two trapezoidal windows was used to search the laser skeleton, and curve fitting was used to mend the laser stripe gap. All the above methods focus on eliminating noise and extracting light stripe from a single image, while few establish frame-to-frame correlation of consecutive video. Interframe correlation can be regarded as a process of target tracking. In the presence of environmental noise, surface reflection and partial occlusion, a combination of spatial and temporal information can be used to detect laser stripe stably and rapidly. In Reference [14], [PERSON] used Kalman filter to track segmented light stripe, and their brightness, length, direction and position are used to establish the correlation of adjacent frames. This method is used to detect obstacles outdoors, but it is only suitable for part of outdoor scenes. To solve the problem that a structured-light sensor is disturbed heavilyby environment noise in snow sculpture measurement, a 3D vision measurement method focused on light stripe extraction and tracking is proposed in this paper, as shown in Figure 2.  
The original images mixed with noise collected by the structured-light sensor are classified based on RGB color space distribution and histogram image at first. Then optimal weight combination of RGB is constructed, and the color image is transformed into a monochromatic value image with high SNR. At last, a Decision Tree Model is established in a spatial and temporal context (STC-DTM) to extract and track the laser stripe. The result of the model after coordinate system transformation is a 3D measurement point cloud. This method improves the performance of a measurement system through image processing technique without changing the components of the structured-light sensor. It is universal for other similar 3D measurements. The remainder of this paper is organized as follows. Section 2 provides details of the method and their algorithms. Section 3 describes the result of experiments for evaluating the accuracy, stability and speed of the method. The final section discusses and concludes the paper. Figure 2: The process of 3D vision measurement based on structured light.