Point cloud segmentation is a crucial step in 3D visual guidance and scene understanding, whose quality directly affects the quality of 3D measurement or imaging. To improve the segmentation accuracy and solve the out-of-bounds problem, this study proposes a point cloud segmentation algorithm for 3D vision guidance. This algorithm generates initial supervoxel data and extracts boundary points based on the spatial position, curvature and normal vectors of the point cloud. Boundary refinement is then performed, which refers to the redistribution of boundary points to optimize supervoxels, by calculating the similarity measure between boundary points and neighboring supervoxels. Ultimately, candidate fragments are obtained based on region growing and merged according to their concavity and convexity to achieve object-level segmentation. Visualization and quantitative comparison show that this algorithm effectively solves the out-of-bounds problem and accurately segment complex point cloud models. The segmentation accuracy is 89.04% and the recall rate is 87.38%.