## 4 Conclusions and Research Recommendations  
Ever since the importance of laser scanning measurement was first recognized, various laser point classification and segmentation approaches have been proposed. However, prior segmentation techniques suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. The segmentation approach newly proposed in this paper provides four main benefits while circumventing the addressed limitations of the existing techniques. (1) Neighborhood definition, which considers both the three-dimensional relationships between random points and the shapes of surfaces, is introduced; this increases the homogeneity among neighbors, which is crucial to the achievement of reliable point attributes. By means of a test using simulated data, it was established and verified that the homogeneity level from the adaptive cylindrical method is much better than that from the spherical one. Also, the high level of attribute homogeneity leads to the better segmentation results. (2) A voting scheme with a 2D accumulator array was introduced to aggregate points having similar attributes into clusters. In this clustering approach, the magnitudes of normal position vectors are utilized as new laser point attributes. Methods for determining the locations of the origins and avoiding the segmentation ambiguity are suggested in this research. The computational efficiency in terms of memory requirement is enhanced, accordingly, by reducing the dimensions of the utilized accumulator array. (3) The clustering procedure proposed in this research simultaneously considers the similarity in the attribute space and the proximity in the object space. Furthermore, points with a higher level of homogeneity take priority over those with lower levels. These two operations in the process of updating plane parameters and defining point members belonging to clusters allow the proposed segmentation approach to provide a fairly robust solution. (4) The methodology proposed works well with both airborne and terrestrial laser point clouds. The results of experiments using real airborne and terrestrial data demonstrate, through qualitative and quantitative evaluations, the outcomes' high level of reliability. The proposed segmentation algorithm, moreover, provides quite high overall correctness and completeness values (_i.e._, 96.89% and 95.84%, respectively). Furthermore, the overall mean value of centroid difference was 0.25 m, and the angle difference was less than \(1^{\circ}\). The performance of the proposed approach was also verified with a large dataset. In the comparison with other approaches, the proposed one showed the better result than others. In conclusion, the methodology proposed herein is a robust and efficient means of abstracting an enormous number of laser points into plane information. Upcoming research will turn its attention more toward origin-location determination methods that do not cause any segmentation ambiguity and accumulator array dimension reduction by using only one origin. Additionally, the proposed methodology's focus on planar patches will be extended to deal with non-planar surfaces of spherical or cylindrical shape. This research will also focus on the automated neighborhood size determination. The authors would like to acknowledge the provision of the Downtown Toronto dataset by Optech Inc., First Base Solutions Inc., York University, and ISPRS WG III/4. The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [33]. This work was supported by 2014 Research Fund of Myongji University. **Author Contributions:** [PERSON] developed the program, mathematical model, and wrote the paper. [PERSON] provided the initial idea of this research and a lot of useful advices. [PERSON] and [PERSON] also helped this research by providing insightful comments. [PERSON] worked for the data collection and experiments.