For the difficulty in balancing compression precision and compression time in the compression of non-uniformly collected point cloud data, a compression method combining density threshold and triangle group approximation was proposed, and the triangle group was constructed by setting the density threshold of non-empty voxels obtained by the octree division in order to realize the point cloud surface simulation. Firstly, the vertices of triangles were determined according to the distribution of the points in the voxel. Secondly, the vertices were sorted to generate each triangle. Finally, the density threshold was introduced to construct the rays parallel to the coordinate axis, and the subdivision points on different density regions were generated according to the intersections of the triangles and the rays. Using the point cloud data of dragon, horse, skull, radome, dog and PCB, the improved regional center of gravity method, the curvature-based compression method, the improved curvature-grading-based compression method, the K-neighborhood cuboid method and the proposed method were compared. The experimental results show that:under the same voxel size, the feature expression of the proposed method is better than that of the improved regional center of gravity method; in the case of close compression ratio, the proposed method is superior to the curvature-based compression method, the curvature-grading-based compression method and the K-neighborhood cuboid method in time cost; in the term of compression accuracy, the maximum deviation, standard deviation and surface area change rate of the model built by the proposed method are all better than those of the models built by the improved regional center of gravity method, the curvature-based compression method, the curvature-grading-based compression method and the K-neighborhood cuboid method. The experimental results show that the proposed method can effectively compress the point cloud in a short time while retaining the feature information well.