The wide applications of 3-D sonar measurements are severely limited by factors such as water column interference, acoustic shadows, complex structures, and scattering noise. Outliers in 3-D sonar data are difficult to remove using traditional methods because the inference factors are different from other types of point cloud data. Therefore, this article presents a novel outlier filtering method by analyzing the sequential characteristics of the 3-D sonar data. First, the underwater point cloud is processed by super-voxel clustering method to decompose complex point cloud structures into several super-voxels with simple structures. Then we convert the point cloud data into subsequence data according to the surveying principle of 3-D sonar scanning and super-voxel results. After that, an anomaly score calculation and anomaly region determination method based on the rectangular information granulation of subsequence data is proposed. This method can capture the intrinsic changing characteristics of each subsequence and has a good recognition effect on the abnormal subsequence. Finally, an outlier detection method combining the Grubbs principle and the abnormal score is proposed and applied to the abnormal subsequences, which considers the distortion not only in the vertical direction but also in the horizontal direction. The experimental results show that the proposed comprehensive filtering method has good accuracy for both horizontal and vertical point cloud data. The average overall accuracy of the test results is 99.1%, and the average kappa coefficient is 0.88, which can be effectively applied to the 3-D sonar point cloud data filtering processing in complex underwater areas.