## 1 Introduction  
Autonomous extraction of objects from terrestrial laser scanners becomes relevant when considering the data volume and the difficulty of interacting with irregularly distributed three dimensional point clouds. Additionally, the growing use of laser scanners for mapping purposes and the growing demand for reconstructing objects in 3D space turn such applications into a necessity. Indeed, object extraction from terrestrial laser scanners has been a research domain in recent years, ranging from reverse engineering problems to building reconstruction, forestry applications, and others [1]. In most cases, a model driven approach is applied, where domain knowledge about the sought after object shape drives the reconstruction and recognition process. [PERSON] _et al_. [2] models industrial installations by using predefined solid object model properties. [PERSON] _et al_. [3] propose an _ad hoc_ approach for tree detection, which is based on trimming the laser data at a certain height to separate the canopy from the ground and searching for stem patches. Such approaches cannot be generalized to other objects, and usually assume a well defined object shape. [PERSON] and [PERSON] [4] extract building elements using a decision rule based approach. In a later publication they recover full building models using a knowledge based reconstruction approach [5]. [PERSON] and [PERSON] [6] reconstruct polyhedral models of the buildings to determine laser points on window edges. The extraction models are focused on a single type of object and assume high domain-knowledge. Alternative approaches, which can still be categorized as model driven, involve generating databases consisting of diverse instantiations of 3D objects. Upon the arrival of new, unseen data, a good matching score is sought between regions in the new data and the database objects. The matching score is usually calculated via key-features and spatial descriptors. Such models are reported in [7,8] and show good results using spin-image based descriptors. Additionally, [PERSON] _et al_. [9] introduce 3D and harmonic shape context descriptors, and [PERSON] _et al_. [10] present a matching score which is based on a robust multidimensional table representation of objects. These methods require generation of massive object instantiation databases and are relatively specific to the modeled objects. As such, they can hardly be considered applicable for natural objects and data arriving from terrestrial scans. Another model driven approach, is based on the extraction of primitives (points, sticks, patches) and modeling their inter-relations as a means to recover the object class. [PERSON] _et al_. [11] extract primitives and then map the links among them as cues for the recognition part. This is demonstrated on scenes containing a few well defined objects, having a relatively small number of primitives (e.g., chairs and tables). Recent works, mainly related to scene understanding from images (e.g., [12, 13, 14]) have demonstrated how the application of segmentation processes for recognition tasks yields promising results, both in terms of object class recognition and correcting the segmentation of the searched objects. Such approaches are categorized as joint categorization and segmentation (JCaS). The main concern of these methods is to correctly categorize the objects of an image towards scene understanding, while compromising the exact segmentation of the object. In contrast, when using terrestrial laser scanning data the objective is to accurately detect all the 3D points related to an object, as a means of autonomous modeling and mapping. Segmentation is a fundamental, mid-level processing phase, which involves grouping pixels containing redundant information into segments. It concerns partitioning the data into disjoint, salient,regions, usually under the assumption that segments tend to represent individual objects or object parts.