## 1 Introduction  
There has been significant interest in developing segmentation methodologies useful for the various applications such as Digital Building Model creation, large-scale ortho-photo generation, city modeling, object recognition, and photorealistic 3D modeling. Since the importance of measurementsfrom laser scanning systems was recognized, diverse segmentation approaches have been suggested and proposed to make their processes more efficient, robust, and automated. Laser point segmentation techniques can be roughly divided into two categories [1]. The first category includes the methodologies that segment point clouds based on criteria such as point proximity and attribute similarity, both locally estimated from the surface. For example, scan line segmentation [2; 3], and the surface growing method [4] belong to this first category. Briefly, the scan line segmentation method first splits each scan line into straight line segments and then, based on a similarity criterion, merges them with adjacent scan line segments. The split-and-merge approach to image segmentation is applied to 3D space. On the other hand, the surface growing method extends seed regions to adjacent points by considering the proximity and similarity of points [4; 5; 6; 7; 8]. In other words, the points that are located near the seed region and that have attributes similar to those of the seed region are categorized into the same group as is the seed region. Also, RANSAC (RANdom SAmple Consensus) [9] is a well-known method to generate a number of model proposals by randomly sampling data points in the object space and then select the model with the largest set of inliers. However, this method is designed to extract a single model. Other alternatives such as Sequential RANSAC and MultiRANSAC algorithms are proposed for multi-model fitting [10; 11; 12]. Additionally, RHA (Residual Histogram Analysis) implements preference analysis which inspects the distribution of residuals of individual data point with respect to the models [13]. Similarly, J-linkage is suggested to avoid a consensus oriented approach by working in the preference space. It adopts a conceptual representation of points. Points belonging to the same structure will have similar conceptual representations [14; 15]. Methods belonging to the second category of laser point segmentation techniques aggregate points with similar attributes into clusters in an attribute space (or parameter space), not, as with the scan line segmentation and surface growing methods just discussed, in the object space. The well-known Hough transform [16] can be extended to deal with 3D surfaces [17]. One laser point on a planar surface in the object space defines a plane in the attribute space. Therefore, points on the same plane intersect in the attribute space at the position that corresponds to the slope and distance of the plane. Within the implementation procedure, the attribute space is quantized and the points voted into the accumulator array cell with the highest number of planes, which represent the largest number of points on the corresponding planar surface in the object space, can be identified and segmented. There is also a publication about applying Hough transform to formulate multi-model fitting as a clustering problem and use mean-shift [18] to identify the modes in this Hough space [19]. Another methodology performs clustering based on an attribute vector consisting of the surface texture measures and/or the parameters of the tangent plane defined from the points in the neighborhood of a certain point [20]. Several variations and applications have been accomplished by [21; 22; 23; 24]. [PERSON] [21] suggested that rather than using an attribute vector with seven parameters [20], a clustering approach, based on an attribute vector with three parameters for each triangle of a Triangulated Irregular Network (TIN)-structure, should be followed. [PERSON] and [PERSON] [22; 23] implemented normal-vector-based clustering using a slope adaptive neighborhood.