Özet:

Semantic segmentation is a data processing method that assigns each labeled pixel to a meaningful class to give meaning to surrounding objects. The development of deep learning-based methods has increased the interest in point cloud segmentation processes. 3D point cloud semantic segmentation in 3D datasets obtained with different scanning tools divides, points with the same feature in the same region are divided into homogeneous regions. The use of semantic segmentation to understand 3D point clouds with 3D objects has been an important start. In particular, the use of deep learning methods has made this area a focal point. In particular, When processing large unstructured 3D point clouds, encountering unique problems on new methods and approaches developed on the based on deep learning has shown that this area needs to be improved. In order to understand the achievements of these new methods, their performance on 3D benchmark datasets ShapeNet, S3dis, ScanNet, SemanticKITTI was evaluated. Important research contributing to the segmentation field with 3D point cloud is analyzed, the advantages, disadvantages and contributions of the proposed methods are presented. The all the presented methods and their achievements on widely used datasets are discussed and he offered information that would lead to future research. 

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