Binocular vision uses the parallax principle of the human eye to obtain 3D information of an object, which is widely used as an important means of acquiring 3D information for 3D reconstruction tasks. To improve the accuracy and efficiency of 3D reconstruction, we propose a 3D reconstruction method that combines second-order semiglobal matching, guided filtering and Delaunay triangulation. First, the existing second-order semiglobal matching method is improved, and the smoothness constraint of multiple angle directions is added to the matching cost to generate a more robust disparity map. Second, the 3D coordinates of all points are calculated by combining camera parameters and disparity maps to obtain the 3D point cloud, which is smoothed by guided filtering to remove noise points and retain details. Finally, a method to quickly locate the insertion point and accelerate Delaunay triangulation is proposed. The surface of the point cloud is reconstructed by Delaunay triangulation based on fast point positioning to improve the visibility of the 3D model. The proposed approach was evaluated using the Middlebury and KITTI datasets. The experimental results show that the proposed second-order semiglobal matching method has higher accuracy than other stereo matching methods and that the proposed Delaunay triangulation method based on fast point location requires less time than the original Delaunay triangulation.