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Recently many applications require an automatic processing of massive unstructured 3D point clouds in order to extract planar surfaces of man-made objects. While segmentation is the essential step in feature extracting process, but bad-segmentation results (i.e. under and over-segmentation) are still standing as a big obstacle to extract planar surfaces with best fit reality. In this paper, we propose an extension of "SEQ-NVRANSAC" approach to avoid the bad-segmentation problems using topology information and intuitive threshold value. First, in order to avoid the under-segmentation problem, we check each one group which resulted from original "SEQ-NV-RANSAC" approach to get all neighbours points which have Euclidean distance less than the threshold value as a one surface group. This process will be repeated until no more points can be adding to that surface group. Then a new surface group will be created to check the remaining points. Second, in order to solve the oversegmentation, we propose three checks; the similarity of normal vectors (NV), the perpendicular distance and the intersection zone using bounding box test.