With the acceleration of industrial modernization, three-dimensional laser point cloud technology has begun to appear in industrial target detection, and the segmentation and extraction of laser point clouds have become the key to industrial detection. Commonly used 3D point cloud segmentation methods, such as region growing segmentation, RANSAC (random sampling consistent) segmentation, K-means (K-means clustering), cannot achieve high-level target segmentation and extraction. In this paper, MEMS (Micro Electromechanical System) 3D cameras are used to collect point cloud data of 4 groups of targets, and the mesh RANSAC segmentation algorithm is used to encapsulate the 3D point cloud of the target, which is rasterized into mesh modules. In order to obtain the final target extraction result and successfully complete the counting statistics, the point cloud in each mesh module is processed with plane rough segmentation, module integration, and fine segmentation on the segmented target using European clustering. Experimental results show that the segmentation completeness of the mesh RANSAC segmentation algorithm proposed in this paper is 91.0%, and the average time is 8.25 s, which is better than the other three traditional algorithms. It also successfully completes the count.