To address the problem of large point cloud data and low segmentation accuracy in indoor and outdoor scenes, we propose a point cloud plane segmentation algorithm based on multiscale region fusion. When using traditional region growing methods for point cloud plane segmentation, the segmentation results are often unstable, and adjacent planes are difficult to separate. In our method, we convert the point cloud data in space into a three-dimensional grid model, and then use the proposed tensor voting method for accurate normal vector estimation. By introducing a multiscale weighted similarity measure to optimize the seed point growing rules, we enhance the accuracy of plane segmentation. Specifically, the algorithm first performs custom grid downsampling and filtering on the original point cloud data. Then different region growing parameters are applied to segment the super-voxel point cloud data at each scale, resulting in multiple segmentation results. The tensor voting is used for estimating the normal vectors of the point cloud, which achieves good estimation performance even at sharp edges. Finally, weights are assigned to similar feature regions, and the similarity measure optimizes the seed point growing rules. With accurate normal vector estimation and appropriate seed point selection, the plane segmentation is more complete. The final segmentation result is obtained by edge preservation and refinement of multiple segmentation results. Experimental results on public and self-collected datasets demonstrate that our proposed method can effectively improve segmentation accuracy while ensuring the real-time performance.