This paper proposes a TLS point cloud and MVS point cloud fusion method based on the graph-cuts algorithm and the guided point cloud filtering algorithm. The graph-cuts algorithm is used to fuse the geometry and colour information of heterogeneous point clouds, and the neighbourhood relationship is considered to ensure the consistency of dense matching point clouds after segmentation. Then, using a neighbourhood point selection strategy, an appropriate proportion of the guide point cloud filtering neighbourhood points are chosen for the dense point cloud near the boundary of the LiDAR point cloud, to realise the guided point cloud filtering weighted by the surface curvature. The experimental results of fusing heterogeneous point clouds with different accuracy, smoothing the gap at the junction of mixed point clouds, and correcting stratification are realised. Experiments show that the proposed method can effectively remove the overlapping and redundant parts of the MVS point cloud and the LiDAR point cloud, as well as improve the accuracy and completeness of the dense matching point cloud, which benefits surface reconstruction.