## 1 Introduction
To reduce the dimensions of the attribute space for greater efficiency or memory requirement purposes, they first carried out the clustering process in a two-dimensional attribute space, utilizing two normal vector slopes in the x and y directions. Afterwards, all of the points with the same slope but different z-intercepts were clustered in the one-dimensional attribute space. [PERSON] and [PERSON] [24] employed an attribute-based clustering approach to the segmentation of planar surfaces. Specifically, they presented an unsupervised planar segmentation process based on fuzzy clustering. [PERSON] and [PERSON] [25] and other two studies [26; 27] suggested similar shape-based segmentation methods for laser scanning point clouds. They proposed methods to calculate the optimal neighborhood size based on the eigenvalues and entropy function, to derive the geometric features for point classification, and to segment the classified point clouds according to the principle of region growing. In our view, most of the above-mentioned segmentation techniques suffer from the following problems: (1) Surface-growth-based segmentation depends heavily on seed point (or seed region) selection, and segmentation performance can be sensitive to the choice of seed points [28]. RANSAC based methods and J-linkage takes greedy clustering approaches which explicitly or implicitly maximize the number of inliers. In these cases, there is possibility that some random model can have a larger number of inlier than the true models [19]; (2) The attribute-space-based segmentation techniques, especially those based on Hough transform, do not consider the connectivity of points, which fact causes the segmentation results to contain many spurious surfaces that do not actually exist [17; 20]; (3) The attribute-vector-based clustering techniques do not involve seed points; however, the efficiency issue arises. Notably, the dimensions of the memory space should be the same as the number of parameters that the utilized attribute vector has. In this regard, with an increased number of attributes utilized in the clustering procedure, the amount of memory required also increases, and tremendously. For example, in the case of [20], a seven-dimensional memory space is required, since his clustering approach is based on a seven-parameter attribute vector; (4) Most of the previous studies pertaining to segmentation techniques have focused on utilization of only one type of dataset (_i.e._, either airborne or terrestrial laser scanning data). Considering the data integration trend for various 3D applications, a generic segmentation approach is essential in order to deal with both the airborne and terrestrial domains. In this regard, the specific objectives of the present research are to propose a segmentation approach that (1) considers both the similarity (in the attribute space) and the proximity (in the object space) of laser points simultaneously; this will remove the problems from the above-mentioned approaches (_i.e._, seed point selection problem, and clustering problem due to greedy inlier maximization); (2) reduces the dimensions of the attribute space for efficiency or memory requirement purposes; and (3) works well with both airborne and terrestrial laser scanner point clouds. This paper commences with a description of the methodology proposed for laser point segmentation. Neighborhood definition, attribute computation based on the defined neighborhood, and the procedures for points clustering using the derived attributes are addressed. Next, test datasets acquired from airborne and terrestrial laser systems are described, after which segmentation experiments carried out in the present study are discussed based on the qualitative and quantitative evaluations. Additionally, the evaluation of the sensitivity of the thresholds was carried out. Finally, in concluding remarks, findings are summarized and future work is recommended.